Integrating Design Optimization in the Development Process ...1322598/...simulation driven design as a tool in the development process. To examine an alter-native way of working for

Linköping University | Department of Management and Engineering Master’s Thesis 30 hp | Master of Science – Mechanical Engineering & Machine Design

Spring 2019 | LIU-IEI-TEK-A--19/03414—SE

Integrating Design Optimization in the Development Process using Simulation Driven Design Authors: Daniel Haraldsson

Marcus Svensson Supervisors: Johan Salomon, Scania CV AB Johan Persson, Linköping University Examiner: Mehdi Tarkian, Linköping University

Linköping University SE-581 83 Linköping, Sweden

013-28 10 00, www.liu.se

AbstractThis master thesis has been executed at Scania CV AB in Södertälje, Sweden.Scania is a manufacturer of heavy transport solutions, an industry which is chang-ing rapidly in order to meet stricter regulations, ensuring a sustainable future.Continuous product improvements and new technologies are required to increaseperformance and to meet markets requirements. By implementing design optimiza-tion in the design process it enables the potential of supporting design exploration,which is beneficial when products with high performance are developed.

The purpose was to show the potential of design optimization supported bysimulation driven design as a tool in the development process. To examine an alter-native way of working for design engineers, elaborating more competitive productsin terms of economical and performance aspects. Furthermore, to minimize timeand iterations between divisions by developing better initial concept proposals.The alternative working method was developed iteratively in parallel with a casestudy. The case study was a suction strainer and were used for method improve-ments and validation, as well as decision basis for the included sub-steps.

The working method for implementing design optimization and simulationdriven design ended up with a procedure consisted of three main phases, con-cept generation, detail design and verification. In the concept generation phasetopology optimization was used, which turned out to be a beneficial method tofind optimized solutions with few inputs. The detail design phase consisted of aparameterized CAD model of the concept which then was shape optimized. Theshape optimization enabled design exploration of the concept which generatedvaluable findings to the product development. Lastly the optimized design wasverified with more thorough methods, in this case verification with FE-experts.

The working method was tested and verified on the case study component,this resulted in valuable knowledge for future designs for similar components. Theoptimized component resulted in a performance increase where the weight wasdecrease by 54% compared with a reference product.

Keywords: Design optimization, Topology optimization, Simulation driven de-sign, Parametric CAD models, Frequency response analysis.

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Acknowledgments

This thesis is the final assignment for master’s studies at Linköping University.The work has been conducted at Scania CV AB during the spring 2019, covering20 weeks of work equal to 30 credits. The thesis was challenging but has given usvaluable knowledge both in an academic and an industry perspective.

We would like to thank Scania and everyone involved in our thesis for makingthis possible. Additional thanks to our supervisor, Johan Salomon, for the help-fulness and engagement to make our time inspiring and pleasant. We are thankfulto perform our work at the NMBO department, the positive attitude and interest-ing discussions has been worthwhile. Another thanks to Mikael Tellner and TinaLouka for valuable insights within the subject of optimization and simulation.

We are also thankful for the feedback and support from our supervisor atLinköping University, Johan Persson. And finally, we would like to show ourgratitude to our opponent, Mattias Andersson, for providing valuable feedbackand dedication of time and effort throughout the thesis.

Linköping, June, 2019

Daniel Haraldsson Marcus Svensson

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Nomenclature

Abbreviation MeaningCAD Computer-Aided DesignCATIA V5 Computer-Aided Three-Dimensional Interactive Application V5DOE Design of ExperimentsFE Finite ElementFEM Finite Element MethodGAS Generative Assembly Structure AnalysisHEEDS Hierarchical Evolutionary Engineering Design SystemKBE Knowledge Based EngineeringMDO Multidisciplinary Design OptimizationNMBO Base engine lubrication systemPD Product DevelopmentSDD Simulation-Driven DesignTO Topology Optimization

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Deliverables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theoretical framework 52.1 Product development process . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Design Process . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.2 Design Paradox . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 CAD-modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.1 Parametrization . . . . . . . . . . . . . . . . . . . . . . . . 72.2.2 Knowledge based engineering . . . . . . . . . . . . . . . . . 92.2.3 CAD model robustness and flexibility . . . . . . . . . . . . 9

2.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3.1 Multidisciplinary design optimization . . . . . . . . . . . . 112.3.2 Topology optimization . . . . . . . . . . . . . . . . . . . . . 12

2.4 Simulation driven design . . . . . . . . . . . . . . . . . . . . . . . . 132.4.1 Finite element method . . . . . . . . . . . . . . . . . . . . . 132.4.2 Frequency response . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Sand casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Thesis methodology 193.1 Pre-study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.1 Literature study . . . . . . . . . . . . . . . . . . . . . . . . 203.1.2 Study of current working method and case study . . . . . . 20

3.2 Development of working procedure . . . . . . . . . . . . . . . . . . 203.2.1 Method development . . . . . . . . . . . . . . . . . . . . . . 213.2.2 Case study validation of working process . . . . . . . . . . . 21

4 Current situation analysis 234.1 Product Development method at Scania . . . . . . . . . . . . . . . 23

4.1.1 General description of product development process . . . . 234.1.2 Design engineer’s role in the process . . . . . . . . . . . . . 24

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4.2 The case study component- Suction strainer . . . . . . . . . . . . . 254.2.1 Description of the component . . . . . . . . . . . . . . . . . 254.2.2 Requirements on the suction strainer . . . . . . . . . . . . . 26

5 Results 295.1 Developed method results . . . . . . . . . . . . . . . . . . . . . . . 29

5.1.1 Phase 1: Start-up . . . . . . . . . . . . . . . . . . . . . . . 305.1.2 Phase 2: Design requirements and concept generation . . . 305.1.3 Phase 3: Detail design and design exploration . . . . . . . . 305.1.4 Phase 4: Design verification and final decision . . . . . . . . 31

5.2 Case study results . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.2.1 Case study-Phase 1 . . . . . . . . . . . . . . . . . . . . . . 315.2.2 Case study-Phase 2 . . . . . . . . . . . . . . . . . . . . . . 325.2.3 Case study-Phase 3 . . . . . . . . . . . . . . . . . . . . . . 335.2.4 Case study-Phase 4 . . . . . . . . . . . . . . . . . . . . . . 415.2.5 Case study improvements . . . . . . . . . . . . . . . . . . . 42

6 Discussion 456.1 Methodology discussion . . . . . . . . . . . . . . . . . . . . . . . . 456.2 Result discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.2.1 Developed working method . . . . . . . . . . . . . . . . . . 466.2.2 Case study discussion . . . . . . . . . . . . . . . . . . . . . 47

7 Conclusion 517.1 Research question 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 517.2 Research question 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 527.3 Research question 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 537.4 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Bibliography 55

List of Figures2.1 Product development process . . . . . . . . . . . . . . . . . . . . . 52.2 The design process paradox . . . . . . . . . . . . . . . . . . . . . . 72.3 Morphological transformation levels . . . . . . . . . . . . . . . . . 82.4 Topological transformation levels . . . . . . . . . . . . . . . . . . . 82.5 Benefits with implementing KBE in the design process . . . . . . . 92.6 LHS example, two dimensional with four sample points . . . . . . 112.7 Illustration of a Pareto front . . . . . . . . . . . . . . . . . . . . . 122.8 Illustration of a topology optimized beam . . . . . . . . . . . . . . 132.9 Meshed triangular elements in a rectangular domain . . . . . . . . 142.10 Representation of the effect of junction and creation of hot spot . . 162.11 Design requirements for sand casting . . . . . . . . . . . . . . . . . 17

3.1 Thesis methodology flow . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1 Product development process at Scania AB . . . . . . . . . . . . . 244.2 Design engineers role in product development . . . . . . . . . . . . 254.3 The reference suction strainer developed at Scania . . . . . . . . . 264.4 The acceptance criteria for reduce the risk of oscillations. . . . . . 27

5.1 Developed method with the different phases. . . . . . . . . . . . . 295.2 Design boundary used as input to Inspire . . . . . . . . . . . . . . 335.3 The optimized solution from Inspire . . . . . . . . . . . . . . . . . 345.4 The first CATIA developed concept of the suction strainer. . . . . 355.5 The optimization architecture used in the case study . . . . . . . . 365.6 The frequency and excitation amplitude in X,Y and Z-direction . . 375.7 The second CATIA developed concept of the suction strainer. . . . 385.8 Description of the parameters used in the concept. . . . . . . . . . 395.9 Maximum stress in Y-direction resulted from frequency excitation. 405.10 Correlation matrix design parameters. . . . . . . . . . . . . . . . . 415.11 Comparison between the developed and original suction strainer. . 42

List of Tables4.1 Material properties for the suction strainer . . . . . . . . . . . . . 26

5.1 The optimization results for the selected design . . . . . . . . . . . 405.2 Results from the verification in Abaqus. . . . . . . . . . . . . . . . 425.3 Comparison of the original suction strainer and the developed concept 43

Chapter 1

Introduction

This master thesis is performed in collaboration with the engine departmentNMBO at Scania CV AB. Scania is a manufacturer of heavy transport solutionsincluding trucks and buses. NMBO are responsible for designing and testing vari-ous components of the base engine, components with the main purpose to lubricatethe engine.

In order to meet a sustainable future within an industry which is changingrapidly and becoming more regulated, continuous development of each componentis required to minimize emissions. By reducing the total weight of the trucks andbuses the fuel consumption is reduced and the load carrying capacity is increased[1]. Optimizing components to increase their performance and minimize weight istherefore desirable, reducing the environmental impact and providing more com-petitive products.

1.1 BackgroundThe most critical factor in profit-driven enterprises is the ability to develop suc-cessful products with an economic success, where the development process is oneof the fundamental parts [2]. Since the product development process requiresman-hours, the total development cost of products can be reduced by changingcompanies working methods. Scania’s development process starts with an initialCAD model from a design engineer followed by analysis from FE-experts. Basedon the simulation results the design engineer makes changes and send the CADmodel to the simulation engineers iteratively until the product meets the require-ments. Then the component needs to be physically tested and approved from anassembly and purchasing point of view until the final design gets approved [3].This development process requires many changes to the CAD model and thereforetime due to demands from the different disciplines. Since the design process istime consuming there is short amount of time for increasing the performance ofthe components even further.

The complexity of products are increasing successively and by implementingdesign optimization at an early stage in the design process, the support for the

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2 Introduction

decision making increases. The design engineer can deliver a better initial pro-posal for the specific product, this induces fever iterations between the differentdisciplines. The procedure of design optimization can to a high extent explore re-lationships between the various properties of a product. By using an optimizationalgorithm together with simulation driven design, the exploration of the designspace increases and simultaneously a product with higher performance can befound and created [4].

1.2 Purpose and GoalsThe purpose of this master thesis is to encourage the usage of design optimizationas a tool in the development process. To examine an effective alternative way ofworking to minimize iterations between design engineers and FE-experts. Anotherpurpose is to implement optimization and simulation driven design in Scania’sproduct development process and show the potential of how to elaborate morecompetitive products, in terms of economical and performance aspects.

The goal is to develop a working process which can be implemented at Scania inorder to work more efficiently and optimize various similar products. This workingprocess will be validated and applied on a case study of an already existing productwhich is a suction strainer.

1.3 Research questionsThe research questions to answer in this thesis work are:

• RQ1: How can design optimization involving CAD and FEM aid the designprocess?

• RQ2: How can topology optimization support concept generation early inthe design process?

• RQ3: What differences and similarities does the developed working processhave compared to a traditional development process at a truck manufacturer?

1.4 DeliverablesDuring the thesis the following deliverables will be performed gradually:

• Investigate the requirements on the case study.

• Develop a working method for how design engineers can work with designoptimization.

• Apply the method and develop an optimized suction strainer.

• Evaluate the developed design suggestion of the suction strainer.

• Evaluate the working method where design optimization has been imple-mented.

1.5 Delimitations 3

1.5 DelimitationsThe thesis work is performed from a design engineers’ point of view. Since the mainfocus is on how design optimization can be used to provide a more competitive finalproduct. The provided working method will focus on how the design engineeringshould work to minimize the required iterations between different disciplines.

The developed working method will be applied on only one case study, butthe method should be applicable on similar products. The development of thecase study component will stop at FE-verification, which means no prototypes orphysical testing will be performed.

For this thesis Altair Inspire will be used for topology optimization, CATIAV5 will be used for CAD-modelling, CATIA Frequency Analysis will be used forcalculations and Heeds will be used as the optimization software. These software’swill be investigated mainly because Scania is using them today which makes iteasier to implement the findings in their development process.

4 Introduction

Chapter 2

Theoretical framework

This chapter describes the theory and literature studies relevant for the thesis andthe studied field. The chapter covers areas such as product development process,CAD-modelling, optimization, simulation driven design and relevant manufactur-ing methods.

2.1 Product development processIn order to develop competitive and profitable products on the market it is ofgreat interest to work efficiently. This chapter describes the fundamental steps ofproduct development and the design paradox.

2.1.1 Design ProcessAccording to Ulrich and Eppinger the product development process is a sequenceof activities consisting of the workflow in figure 2.1.

Figure 2.1: Product development process [5].

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6 Theoretical framework

The product development process starts with the planning phase which definesthe opportunities and evaluates the market objectives and potential technologies.When ending the planning phase, the mission statement of the product should bedefined, containing the business goals, target market and limitations [5].

After the planning phase ends the concept development phase starts. Thecrucial task in this phase is to identify the needs and generate several conceptswith desired performance. Followed by selecting one or a few concepts for furtherdevelopment and testing.

When one concept has been chosen the process continuous into the system-leveldesign where the architecture of the product is determined. The product conceptis decomposed into several subsystems and parts, were the crucial components arepreliminary designed. The assembly scheme for production should also be definedin this phase.

The following phase is detail design, where each part is fully specified in termsof material, geometries and tolerances. The manufacturing preparation of thecomponents should also be completed in this phase and production cost should bedefinite.

The next step is testing and refinement phase were the product performance isevaluated by simulations or physical testing making sure it will fulfil the require-ments before entering the final phase which is production ramp up.

In the production and ramp up phase the assembly staff are educated and ifthere are any production related issues left they should be solved before the fullscale production [5].

2.1.2 Design ParadoxThe design paradox describes the evolvement of the product development process.When a project starts the engineers have brief knowledge about the design prob-lems to solve. During the early phase of the product development process thereare large possibilities to make changes since few decisions have been made. As theproject progresses the knowledge of the subject increases. However, the possibilityof design changes are very limited and costly due to already established decisions[6][7]. Generally, wrong decisions in the conceptual product development can in-crease the manufacturing cost by more than 60%. It is therefore important to mapand solve potential problems as early as possible [8]. This dilemma is called thedesign paradox and is illustrated in figure 2.2.

2.2 CAD-modelling 7

Figure 2.2: The design process paradox [7] .

2.2 CAD-modellingIn order to work more efficiently with product development there are several toolswhich can be used. This chapter describes how to work smarter with CAD-modelling, integrating information by knowledge based engineering (KBE) andhow to make models more usable and flexible using parametrization.

2.2.1 ParametrizationOne way of making the product development process more time efficient is todevelop reusable CAD-models. This can be done by controlling the CAD-modelwith parameters which are non-geometric features. The basic idea is to be able toreuse the CAD model by modifying its geometry with minimal effort [9]. Makingit possible to generate several versions in for example a product family [10].

There are various levels of parametrization which are divided into morpho-logical and topological transformations. Morphological transformations representchanges in shape while topological transformations involve positioning of objectsand features within a CAD-model [11].

As figure 2.3 shows, the morphological transformation contains four levels.Fixed object is the lowest level which corresponds to a model without the abilityto change shape. The parameterized object level represents models with possibilityto change shape with the help of parameters, lacking relations between the variousparameters. The equation-based relation level involves dependencies between pa-rameters. The top level of morphological transformation is script-based relationsdescribing relations with programming [11].


Figure 2.3: Morphological transformation levels [11].

Figure 2.4 illustrates the topological transformation levels, which concernsadding, removing and changing instances in a CAD-model. The levels in topo-logical transformation increases the complexity in how instances are handled frommanually instantiated to fully automated instantiation [11].

Figure 2.4: Topological transformation levels [11].

2.2 CAD-modelling 9

2.2.2 Knowledge based engineeringEngineering knowledge is a fundamental part when developing products were thetraditional way of storing the knowledge have been various books, technical docu-ments and manufacturing drawings. Knowledge based engineering (KBE) has thepurpose of embedding the knowledge into suitable software’s making the knowl-edge reusable and the development process faster. By integrating knowledge intechnology platforms the possibility for effective collaboration between differentdisciplines increase because of easier access [12] [13]. Within the area of CADmodelling example of knowledge of interest to integrate can be various rules andguidelines about the manufacturing method, geometry and other crucial informa-tion for the specific components [14]. KBE also act as a support for design op-timization since it connects design parameters and functional requirements withoptimization formalizations [15].

Implementing knowledge based engineering in the design process can reducethe development time needed since the needed time for routine work decreases, seefigure 2.5. Decreasing the amount of time needed for routine tasks enables moretime for innovative work in the design process [14].

CAD software’s often include KBE modules since it is a crucial part in thedesign process where repetitive work can be minimized. One example of a CADsoftware offering modules for enabling the potential of KBE is CATIA [14].

Figure 2.5: Benefits with Implementing KBE in the design process [14].

2.2.3 CAD model robustness and flexibilityDesign optimization and knowledge based engineering requires flexible and robustCAD-models. Flexible in the sense it has the possibility to adapt and change


shapes and configurations, making it possible to have a product family covered bya CAD-model. The CAD-models’ flexibility therefore increases by the amount ofproduct variations they can represent. Robustness can be explained as the abilityfor a CAD-model to be flexible without encountering instability errors. The higherthe robustness, the fewer error occurs [11].

Developing advanced CAD-models which are flexible and robust for designoptimization is an experimental process, since there is no specific recommendationon how to successfully develop CAD-models with these qualities. However, thereare ways to measure the robustness, so improvements can be made until the modelmeets the required level for successfully carrying out optimization.

Robustness can be calculated using equation 2.1 where RSc represents therobustness and the index Sc indicates robustness for the sub space. NFailurescorresponds to the number of trials resulting with errors and NUpdates are thenumber of trials. Note, the number of trials needs to be sufficiently large in orderto get a robustness with statistical relevance [11].

RSc = 1 −NF ailuresNUpdates

(2.1)

2.3 OptimizationOptimization can be explained as finding the best solution among several feasibleones. The feasible solutions are all solutions not violating any constraints. Ob-jective function is the function describing the desired property which can eitherbe minimized or maximized. Where the objective function can represent vari-ous properties such as weight, efficiency and manufacturing cost. The constraintsare properties expressed with functions which have limits not to be exceeded, forexample maximum allowed stress or minimum flow performance [16].

One way of describing the mathematical formulation of an optimization prob-lem can be expressed as below. Where f(x) is one or several objective functions,g(x) and h(x) are constraint functions and the vector x represent the design vari-ables [16].

Objective function(s):f(x)k k=1,2,3,..., K

Subjected to:g(x)i ≤ 0 i=1,2,3,..., m

2.3 Optimization 11

Within optimization experiments are changes of inputs by a given rule and iden-tifying the correlating output. Design of experience (DOE) is the umbrella termfor techniques to efficiently guide selection of experiments [17]. Latin Hypercubesampling is one DOE technique which is inspired by a mathematical combinationnamely the Latin Square, where an N x N matrix is filled with N objects so theycover each row and column in the matrix, see figure 2.6. This technique can beadapted to cover and explore the design space within optimization [18].

Figure 2.6: LHS example in 2D with N=4 [18].

2.3.1 Multidisciplinary design optimizationWhen developing complex engineering systems with several interest from two ormore disciplines, multidisciplinary design optimization (MDO) is a useful tool touse early in the development process when there is still a high level of design free-dom. The MDO can map the relations and dependencies between each subsystem,enabling a greater understanding of the system characteristics [19]. The gainedknowledge serves as a well-founded base to make decisions from in the design pro-cess. MDO facilitates more design exploration which increases the possibility tofind optimal solutions [20].

Using concurrent design where interest from all disciplines are combined, com-promised global optimums for the system can be reached. Instead of finding opti-mums for each subsystem which most likely will not collaborate due to conflictingrelations [20]. Since MDO handles conflicting objectives from various disciplines,no single optimum exists but instead several Pareto optimal solutions. The Paretofront illustrates the trade-off between the objectives where each design solution onthe Pareto front is a non-dominated design and is therefore an optimal solution[21]. Figure 2.7 illustrates an example of a Pareto front where the non-dominatedsolutions are Pareto optimal and the other solutions are feasible dominated solu-tions [16].


Figure 2.7: Illustration of a Pareto front [16].

2.3.2 Topology optimizationTopology optimization (TO) is a general form in structural optimization, themethod places materials anywhere inside a boundary to achieve optimal perfor-mance solutions with a given set of conditions. In a three-dimensional case thebasic idea is to let the finite elements in the design boundary take values 0 or 1,when the value is set to 0 no material is placed and vice versa. The number ofelements are minimized to achieve the optimal performance with its given designconstraints [22]. An example where TO has been implemented can be seen infigure 2.8.

There are areas where topology optimization is beneficial, for instance whena new product is developed it is often designed from different concepts with apotential of satisfying the requirements. These concepts are often developed basedfrom existing components or from experience. When a product in a new field isdeveloped, TO can be implemented early in the design process to evaluate differentpossible solutions. The benefit with the method is the usage of few number of inputvariables needed for the algorithm to find the first solution. The input data canfor instance be the boundary conditions and the given sets of loads [23].

2.4 Simulation driven design 13

Figure 2.8: Illustration of a topology optimized beam [23].

2.4 Simulation driven designThe usage of simulation driven design (SDD) can broaden and explore scenariosto evaluate different design opportunities. The design performance can increaseas well as enable verification of physical properties earlier in the design process[24]. Simulation-driven design is defined by Sellgren [25] as, ”a design processwhere decisions related to the behaviour and the performance of the design in allmajor phases of the process are significantly supported by computer based productmodeling and simulation”.

The use of simulation can be implemented in several disciplines, such as fluid-and solid mechanics. The main advantage is enabling developers to test the func-tionalities and receive feedback iteratively before significant development commit-ments are made [26]. Investigated systems can often be very complex because ofinteraction between several physical domains, the numerical models used in simu-lations can be used to decrease the complexity and give a solution to the system.However, physical testing of the system is often beneficial to test the hypothesisand verify the results [25].

2.4.1 Finite element methodThe fundamental reason to use the finite element method (FEM) is to find solutionsto a complex problem, the solution approximates the exact solution but is oftenadequate to most existing problems. Because the existing mathematical tools arenot sufficient to find exact solutions. By using more computational efforts into theproblem, the approximated solution can be improved and refined in a cost- andtime-effective way [27].

The finite element method is a key feature in most development processes, andis often implemented in stress analysis, thermal analysis, fluid flow analysis, etc.The analyst determines for instance displacement in stress analysis or the heatflux in a thermal analysis.

The general working principle for the method is to divide the problem domaininto small interconnected subregions called finite elements, see figure 2.9 [28]. Thismakes the theory applicable to a wide range of different boundary value problems,a boundary value is described as the existing solution in the domain of a body to-


gether with a set of constraints called boundary conditions. There exist three maincategories of boundary value problems, equilibrium and steady-state, eigenvalueproblems and transient problems. An equilibrium problem is often solid mechanicproblems where the displacement or stress distribution is defined. In an eigenvalueproblem the natural frequencies are calculated. The type of transient problemsare time-dependent and are used for instance when there is interest of finding theresponse of time-varying forces [27].

Figure 2.9: Meshed triangular elements in a rectangular domain [28].

According to Liu and Quek [28], the procedure of using the finite elementmethod consist primarily of four steps:

1. Modelling of the geometry

2. Meshing of the geometry

3. Define material property

4. Specification of boundary and loading conditions.

In engineering design the geometry is created in a CAD-software. However,the ”real” CAD-model created by the designer is often very complex and need tobe simplified in order to perform a good analysis. The model can then be meshedwere the geometry is divided into small pieces. It is important to have the rightcoarse of the mesh, providing an accurate result while decreasing the simulationtime. The type of material and which load conditions the geometry is subjectedto must be defined before the analysis is performed [28].

2.4.2 Frequency responseStructures and components can under certain conditions start to vibrate either in aconstant or exaggerated motion. Resonant vibration is characterized by modes andthe phenomena is caused due to combination of the materials elastic and inertialproperties. Problems regarding the topic is common in machinery environmentduring operation [29].

Vibrations are caused by a combination of resonant vibration and a forcedvibration. The forced vibration can be caused by unbalances, external loads,

2.5 Sand casting 15

ambient excitation or internally generated forces. A structures level of deflection,strain and stress, caused by static loading is typically amplified significantly byvibration response when subjected to resonant vibration.

Modes are implicit properties of a structure and they are determined by thestructural stiffness, the mass and damping properties. Each individual mode isdefined by mode shape, mode damping and the natural frequency. Structuresmodes will therefore change if the boundary conditions or the material propertiesare changed.

Frequency response function measure dynamic properties in mechanical com-ponents. The measurement can be defined as resulting velocity, acceleration ordisplacement response per given excitation force as an input. The response curveof a structure is represented by a summation of the response curves for each mode.

A mechanical structure is sensitive at certain natural frequencies (modal fre-quencies) since the modes will act as amplifiers, meaning a small input force causesa very large response. The critical regions on a frequency response curve are thesection where high amplitude is combined with high frequency [29].

2.5 Sand castingOne of the most popular manufacturing method is sand casting. This is due to thediversity of different materials, for its cost effectiveness and because of its greatgeometric freedom capability [30]. The casted parts can vary in size and weightfrom a dozen of grams to several tons. Sand casting is characterized as the useof sand as mold material with a suitable bonding agent, which the desired shapeof the component can be created in. Common materials which are widely usedin sand casting are cast iron, magnesium and aluminium. The method is oftenused when prototypes are made because of its inexpensive molds compared withother sufficient casting processes. However, the method is only favourable for lowvolume production [31].

The manufacturing method can receive sufficient tolerances and surface finishfor a large field of applications. For an aluminium component with a size around1500 mm an expected tolerance will be around ±0.75 mm with a surface finish of50-150 µm [32].

When designing sand casted components there are several factors to consider.There is no manual to follow strictly because of different geometries creates dif-ferent behaviours, therefore guidelines or rules has been developed to assist in thedevelopment process [33].

Some design considerations to consider when developing sand casted compo-nents are [30][33]:

• Use simple flowing lines with minimum projection in opposite directions.

• Build in strength of design instead of adding material, such as ribs to stiffenand strengthen castings.

• Minimize the need of usage of cores, number of cores increase complexityand expense.


• Avoid sudden changes in section thickness, this occur unintended in junctionsand will create hot spots, see figure 2.10.

• Avoid sharp corners and use fillets in a high extent.

• Extensive horizontal flat surfaces should be avoided, because of warpage.

• Long transport sections for the melt must have a suitable wall thickness.

• Seek after a straight parting line for the component.

• Design it to be easy to pour and have into account where the ricer will belocated.

Figure 2.10: Representation of the effect of junction and creation of hot spot [31].

However, there exist dimensioning rules to follow when designing componentsfor sand casting. For all vertical surfaces a positive draft angle should be used,otherwise the detachment of the molded component can be difficult. The recom-mended draft angle should be somewhere between 0.5°to 2°. To stiffen or strength-ening castings ribs should be used, the size of rib is dimensioned depending on thewall thickness. The rib thickness should be somewhere between 1 to 1.5 times thewall thickness. The wall thickness cannot be smaller than 3 mm for sand castedcomponents. In the design sharp edges shall be avoided, when an L-junction iscreated the preferable inner radius can be set to the wall thickness and the outerto two times the wall thickness [30]. A summarize of the dimensioning rules canbe seen in figure 2.11.

2.5 Sand casting 17

Figure 2.11: Design requirements for sand casting.

Chapter 3

Thesis methodology

This chapter describes the methodology and working procedure adapted in thisthesis. The work was divided into two main parts, first a pre-study was performedand based on those findings the main work and result generation were conducted.The general workflow outline for the thesis is presented in figure 3.1.

The first phase, pre-study, included a literature study in the investigated field,as well as a current situation analysis of Scanias working method to get knowledgeand find improvement potentials. In the thesis a developed method was testedand improved with a case study component. The requirements on the case studywas in this stage defined and documented.

The second part of the work was the primary result generation, this process wasaccomplished in an iterative manner, where the working method was developedin parallel with the case study validation. Finally, the thesis result was presentedwith a final developed working method as well as a finalized case study.

Figure 3.1: Thesis methodology flow.

3.1 Pre-studyTo provide a knowledge base for this thesis, studies of relevant literature in thefield and former thesis work was realized. A study of the current working method

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20 Thesis methodology

and case study requirements was also conducted.

3.1.1 Literature studyTo provide a knowledge foundation about the areas covered in the thesis a litera-ture study was performed. The gained knowledge served as a support for makingadequate decisions throughout the thesis, as well as answering the research ques-tions. Previous work and research within the relevant areas were studied. Theinformation was gathered mainly from technical reports, books and scientific arti-cles. However, some information about how Scanias current working process camefrom their internal documents such as standards and guidelines.

Previous thesis work within similar field were studied with the main purposes ofnarrowing down the scope of the thesis, in addition to absorbing their conclusionsand findings. The study of previous thesis work was not used as a base for thetheoretical chapter of the thesis. It was instead used as an inspiration how to planthe project and giving ideas of suitable research questions for the field etc.

3.1.2 Study of current working method and case studyScanias product development process was investigated by reviewing their com-pany standards within the subject and by confirming the gained information withemployed design engineers. The investigation of how design engineers work atScania with product development served the purpose to be used as comparisonwith the developed working method. The information about how Scania workswith product development can be seen in detail in chapter 4.1.

To provide knowledge about the suction strainer a thorough investigation aboutthe case study component was done. The investigation covered the basic function,sub features and constraints due to the operating environment of the component.The investigation also covered the interfaces not allowed to change, manufactur-ing method and associated geometry limitations etc. The information about thesuction strainer can be seen in chapter 4.2.

3.2 Development of working procedureThe main purpose with this thesis was to provide an alternative way of workingwith product development as a design engineer using design optimization. Hencea working procedure was developed and presented for Scania. The thesis method-ology used for developing the working procedure is illustrated in figure 3.1. Theworking procedure was carried out in an iterative approach, where an alternativemethod was realized and tested on a case study to gather valuable information.The method was then improved until it delivered as anticipated. The final methodis presented and described in chapter 5.1.

3.2 Development of working procedure 21

3.2.1 Method developmentA working method was provided to aid a more efficient way of working with prod-uct development from the perspective of a design engineer working with designoptimization. The method described the fundamental parts to think about in or-der to successfully carry out optimization and simulation driven design. With apurpose to find better solutions earlier in the development process, with less re-sources required. The method serves as a material which a design engineer shouldbe able to use for future work. Note, the method is not a thorough manual to beused as a step by step process.

3.2.2 Case study validation of working processIn parallel with the development of the working method, the suggested procedurewas validated with a case study. This gave a standing point for reflecting overdecisions made when applying the developed method on the case study. Hopefully,providing design engineers deeper understanding when using the method from thisthesis in future development projects.

22 Thesis methodology

Chapter 4

Current situation analysis

This chapter describes the current product development method at Scania and apre-study of the investigated case study component. The information presentedin this chapter has been collected from internal sources at Scania and publishedreports in the field.

4.1 Product Development method at ScaniaThis section describes the product development at Scania and how different disci-plines work in a cross-functional organization to deliver desired products.

4.1.1 General description of product development processThe product development process (PD) at Scania has been developed to be ableto handle a global perspective and in high extent promote parallel work as muchas possible. This is a cross-functional organization system, which enables highinteractions between different departments and specializations to achieve commongoals. The PD process can be seen in figure 4.1, and describes the different stages.The main activities are Concept development, Product development and Productfollow-up, where the project progresses from a concept or idea to a fully devel-oped product implemented in production. Advanced engineering and research isoften used when new areas of investigation is necessary. A team of researchers,often external sources, and experienced engineers develop a solution to the specificproblem [3].

In the first stage, concept development, a group with high degree of cross-functionality investigates business possibilities and the different technical solutions.An iterative approach is used between disciplines to find the best working conceptand to utilize as much knowledge as possible. Here a lot of preliminary CADmodels and simulations results are compiled to find a suitable solution. Finally,the product requirements are set, and a detailed design needs to be developed.

Next in the PD process, product development, a finalized product needs tobe prepared for the production. The previous work is considered and further

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24 Current situation analysis

Figure 4.1: Product development process at Scania.

development is performed. An iterative approach between CAD, simulation andphysical testing is used to reach desired goals and to minimize uncertainties. Theproduct specifications can be updated along the way, which increase number ofiterations. When the developed product has passed the requirements and all tests,it is ready to be produced.

The product is then followed up to maintain and update if necessary. There aredifferent assignment tasks in this phase, it can be a quality check, product changerequest, design adjustments or cost reduction. Documentation about importantlearning from the development is compiled, it is called lesson learning and is usedto minimize appearance of similar problems and to transfer knowledge betweenemployees. This phase improves Scania’s opportunities to deliver more competitiveproducts to their customers [3].

4.1.2 Design engineer’s role in the processThe design engineer at Scania has a wide range of activities, depending on the stageof the project and type of product. They often work as coordinators between thedifferent disciplines during the development process for the specific product, seefigure 4.2. The design engineer is involved during the whole process from concept tofinal product, and has the main activities to specify requirements, creating CAD-models and update existing products. To be the coordinator require knowledgefrom the different disciplines more specific FEM and CFD departments. The workbetween the disciplines are an iterative process, which means when a CAD-modelis created and analysed with CFD- and FEM-simulations. The results are gatheredand necessary improvement is made to the product iteratively [3][34].

4.2 The case study component- Suction strainer 25

Figure 4.2: Design engineers role the in product development process [34].

4.2 The case study component- Suction strainerIn order to have a well-functioning engine the lubrication is a key factor. Thisis mainly executed by the oil. The primary purpose of oil is to minimize frictionand wear between components and to dissipate heat from fundamental parts. Themotor oil cleans the engine and collects harmful particles, and it is therefore im-portant to have a high exchange of oil cleaned successively by the oil filter [35].When the oil is not used, it is stored in the oil pan. When the engine is operating,it is picked up with a suction strainer and transferred into the engine. The suc-tion strainer is therefore a fundamental part to ensure good and consistent flow ofoil. The performance of the component depends on several factors and need to befulfilled to reach desirable results.

4.2.1 Description of the componentThe main function of the suction strainer is to ensure a good oil pick up fromthe sump into the engine internals. The oil-pump creates a suction force andtransport oil through the inlet and outlet, before feeding it into the engine, seefigure 4.3. At the end of the inlet a filter is attached to prevent harmful piecesmaking the way into the engine. The suction strainer is mounted below the engineblock with three bolts, the placement of these mounting points are far away fromthe oil inlet pipe. The inlet pipe placement needs to be in this position in order tominimize risk of oil starvation, when the vehicle is in critical places such as inclines.The connection between the mounting points and the suction pipe are designedto have a rigid structure withstanding the external conditions and to ensure aphysical fitment inside the oil pan. The component also has run-off angles andholes at suitable locations to ensure no creation of oil pockets. Another function


is measurement of the oil-level which is accomplished by inserting an oil-stick inthe oil-stick container, see figure 4.3.

Figure 4.3: The reference suction strainer developed at Scania.

The suction strainer is low volume production item and is therefore manufac-tured using sand casting. The material used is aluminium and has the propertiesshown in table 4.1.

Table 4.1: Material properties for the suction strainer.

Properties ValueMaterial EN AC-43100 SFDensity 2770 kg/m3Young’s modulus 71 GPaPoisson ratio 0.33Yield strength 150 MPaFatigue limit 45 MPa

4.2.2 Requirements on the suction strainerThere are several restricting factors to consider when developing the suction strainer.The manufacturing method, sand casting, has geometric freedom capability butthere are still rules to follow, see section 2.5. The geometric boundary for thesuction strainer is limited by several surrounding components in the engine, such

4.2 The case study component- Suction strainer 27

as oil pan, balancing shafts, engine block etc. The design was also limited by thefixed position of the mounting points, oil pick-up location and oil-stick container.

Since the component is placed inside an engine the loading conditions it mustwithstand are vibrations during runtime. The vibrations induce fatigue on thecomponent and frequency calculations had to be performed to find stress concen-trations and fatigue limits. The engine has a specific frequency spectrum whichdefines the acceleration excitation which is acting on the component. These valueshave been measured by physical testing for Scanias various engines. Generally, theengines does not exceed a frequency range of 300 Hz, see figure 4.4. An investi-gation of the eigenfrequency of the suction strainer must be performed. If thenatural frequency is below 300 Hz the component can start to oscillate and getdamaged. By using this limit the probability of oscillations for the component de-creases significantly. The component must also have a adequate stiffness to ensurea low deflection.

Figure 4.4: The acceptance criteria for reduce the risk of oscillations.

Chapter 5

Results

This chapter presents the gathered results from the method development and theresults from the newly developed case study component. Followed by performancecomparison between the developed and the original component.

5.1 Developed method resultsThe developed working method for implementing design optimization and simu-lation driven design in the development process is illustrated in figure 5.1. Themethod is divided into four phases, each phase includes relevant tasks. The taskscould be conducted simultaneously or sequentially, before a new phase in the prod-uct development process starts.

Figure 5.1: Developed method with the different phases.

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30 Results

5.1.1 Phase 1: Start-upThe initial phase when developing a component is to identify what type of projectto be executed, for example a concept development or detail development project.Different types of projects give diverse prerequisites in terms of design opportu-nities. From a design optimization perspective, a detail development project willmost likely encounter a more restricted design space compared to a concept de-velopment project. Since decisions about neighbouring components for a detaildevelopment project affects the design freedom greatly. While the design freedomin the start of a concept development project is in general greater when a projectstarts from scratch.

The start-up phase contains identifying all the disciplines of interest for theproject. The goal of the phase is to define the problem, identify the goals, theinterests from each discipline and potential conflicting interest. When the phaseis finished the departments should have a common idea of the expected outcomesand what each department is expected to deliver. The project plan should alsobe created during phase one, which should make all stakeholders familiar with thetime scope and the amount of resources for the project.

5.1.2 Phase 2: Design requirements and concept generationIn this phase all the requirements on the product should be defined. This could forinstance be type of manufacturing method and its limitations. Other examples ofrequirements can be constraints in terms of minimum allowed eigenfrequency, flowperformance or maximum allowed stress for the specific product. Setting up allthe design requirements and load cases in the correct way are crucial. If the set-up differs from the real environment the risk of component failure or inadequateperformance increase.

Next step in the phase is to define and create a model of the design space andthe fixed geometries as well as product functions. Which serves the purpose fortopology optimization, giving the design engineer an idea of where the material isneeded and how the initial concept could look like. The fixed geometries are thefunctionalities not allowed to change when performing the topology optimization.By setting up load cases and supports the component can be optimized with theobjectives of maximizing the stiffness or minimizing the mass. The result of thetopology optimization serves as a conceptual proposal of the structure, whichfulfills the desired requirements. This concept serves as a decision basis for furtherdevelopment.

5.1.3 Phase 3: Detail design and design explorationFrom the previous phase the design engineer should obtain a concept from thetopology optimization, regarding how the product could look like along with allthe requirements. The geometry should then be modelled in a CAD software in aflexible and robust way. The CAD model should be parameterized in order to beenable shape optimization, meaning various dimensions of the geometry are ableto vary using parameters. The manufacturability of the concept should be verified

5.2 Case study results 31

in some manner, making sure the simulations are not performed in vain with aconcept not manufacturable.

The CAD model should then be analysed with relevant simulation models, ineither the used CAD software or in another software for simulations. By setting upthe simulation framework with relevant load cases and supports the optimizationsoftware can then explore the design space by varying the parameters in the sim-ulation model. Resulting in various design suggestions to be compared in termsof the desired performance. One of the design suggestions will be chosen andanalysed further for verification.

5.1.4 Phase 4: Design verification and final decisionIn the final phase the concept from the shape optimization needs to be verifiedwith calculation experts. The experts should perform more computational expen-sive analysis, making sure the selected concept fulfils requirements of for exampleeigenfrequency, fatigue and flow etc. The main goal with the developed method isto provide a better initial proposal to verify with the calculation experts, not toeliminate the need of verification from these disciplines.

When the concept is confirmed by the calculation experts, the next step isto make physical testing and to verify the results with the responsible discipline.The physical testing will vary greatly depending on the type of product and someproducts might not need this step. When the concept has passed the verificationwith the disciplines mentioned above, the stakeholders should make a final decisionregarding the products future.

5.2 Case study resultsThe developed method, described in chapter 5.1, was implemented on a case studycomponent. The original component and its requirements has been described indetail in chapter 4.2. The case study results are described in the same order asthe proposed method.

5.2.1 Case study-Phase 1The goal with this case study was to develop a suction strainer design with lowerweight compared to an already existing component, the newly developed com-ponent should withstand the given requirements. The question to be answeredwas:

• Can an alternatively suction strainer be developed with lower weight andstill meet the requirements?

The disciplines involved for this project was primarily design engineers and cal-culation engineers with competence in the field of vibrations- and frequency cal-culations. The reason why those disciplines were involved was because of thecomponents operating environment. If this project was to develop a totally new

32 Results

concept the discipline of CFD should be involved as well, but because of perfor-mance satisfaction for the already existing fluid pipes in the design those was notinvolved.

In this phase it was decided that the design engineer should do most of thedevelopment work and the calculation engineer should in the end verify the pro-posed concept. The calculations performed had a level of difficulty which in someextent was executable for a design engineer to give a good result estimation.

5.2.2 Case study-Phase 2In this phase of the project the goal was to compile a preliminary concept showingthe functions and the possibilities. The outcome from this phase was used as thedecision basis for detail development.

Define design boundary

The process started by investigating the already existing suction strainer and defin-ing where the biggest potential of improvement existed. Since the surroundingcomponents was not changeable, the placement for mounting holes, oil stick con-tainer and suction pipe was fixed. This gave the biggest potential of improvementto be the structure in between those parts.

The existing suction strainer was developed to not interfere with surroundingcomponents, the acceptable design boundary for the structure was defined bydrawing a geometrical boundary in CATIA where material can be added withoutinterfering, see figure 5.2. The interfering components for this case was balancingshaft from the top and swash plate from the bottom, other small componentssuch as screws had to be considered. The defined boundary as well as the fixedgeometries, mounting points, oil-stick container, suction pipe, was then used asthe input to the topology optimization.

Topology optimization in Altair Inspire

The topology optimization was performed in Altair Inspire, the software can withfew inputs give a solution to the problem based on the geometry created in CATIA.When the CATIA model was created and saved it was loaded into Inspire and theoptimization problem could be defined.

First, the created design boundary was defined as the acceptable design spaceto place material in as well as creating and specifying the type of material for thecomponent. The material for this component was aluminium, EN AC-43100 SF,the specific material properties can be seen in table 4.1 in chapter 4.2.1. The loadcases were then added to the model, mounting holes were set to fixed displacement,and loads was set in X, Y and Z-direction at the suction pipe. This placement offorces gave a good distribution of material and was a approximation to give thecomponent high stiffness.

Inspire has a built-in function to take manufacturing methods into account, thecomponent was going to be sand-casted which set requirements on the possibilityfor easy removal from the mold. By placing a shape control in the software at a


Figure 5.2: Design boundary used as input to Inspire.

suitable partition plane for the component, the optimization algorithm took themould removal into account.

The optimization problem was then defined with the input to maximize thestiffness as the objective. The problem was constrained by a minimum allowedeigenfrequency of 300 Hz, as well as a minimum thickness of 5 mm, see chapter4.2.2.

The result from the optimization run can be seen in figure 5.3. The materialplacement, in orange, had a structure with several branches from the mountingpoints to the suction pipe. The oil-stick cup was connected to one of the mountingholes. The optimized solution was used as a baseline for the parametric CAD-modelling in CATIA, the model was inserted into CATIA for simplifying the re-creation of the optimized solution.

5.2.3 Case study-Phase 3The suction strainer concept was developed and had to be prepared for shapeoptimization. The basic idea was to mimic the Inspire optimized shape and createa parametric CAD model.

Parametric CAD model

When the parametric CAD model was developed, the parameters were chosen sothe model had a certain geometric freedom. The geometric freedom was requiredfor enabling exploration of designs during shape optimization later in Heeds. The

34 Results

Figure 5.3: The optimized solution from Inspire.

parameters were enabled to vary the elliptic cross sections of the braces at variouspositions. The angles of where the braces were connected to the fixed geome-tries and the ratio of where brace 2 connected to brace 1 was also varied with aparameter. The result of the concept is illustrated in figure 5.4.


Figure 5.4: The first CATIA developed concept of the suction strainer.

The optimization problem and architecture

Before the optimization algorithm could find different solutions to the parametricCAD-model, the optimization problem had to be defined and concretized. The ob-jective for the optimization was set to minimize the weight of the suction strainer,and had to be constrained by the eigenfrequency, fatigue stress and the geometricfreedom.

The eigenfrequency had to be at least 300 Hz for the first eigenmode, the reasonwhy this constraint excised was to avoid natural oscillations of the suction strainersince frequencies in the engine during operating typically does not exceed thisvalue, see chapter 4.2.2. To fulfil the fatigue constraint the induced stress in thecomponent was not allowed to exceed 6 MPa, which was based on the fatigue limitfor the material and safety factor. The optimization problem included a geometryinterference check, ensuring the suggested design did not interfere with surroundingcomponents. A summarization of the optimization problem is illustrated below.

36 Results

The general optimization problem:

Minimize:Mass [kg]

Subjected to:Eigenfrequency > 300 HzFatigue limit, σf < 6 MPa

Geometry interference check = OK

To solve the optimization problem the practical approach was described inan architecture of the information flow, see figure 5.5. The central part is theoptimization algorithm and was handled by Heeds. The software was assigned tomanage the design parameters range of acceptable minimum and maximum values.As well as the responses from the different conducted analysis from CATIA.

Figure 5.5: The optimization architecture used in the case study.

The first CATIA analysis was a geometry check and consisted of a Clash Anal-ysis, secondly a Frequency Case and three Harmonic Dynamic Response Cases wascalculated. The Clash Analysis was performed to make sure the suggested designsfrom Heeds did not result in interference with the surrounding components in theengine. If the Clash analysis found an interference between the suction strainer andthe nearby components, the design was considered to be failed and the FrequencyAnalysis and the Harmonic Dynamic Response Cases were not computed.

The Frequency Case calculated the eigenmodes for each design. In the Fre-quency Case the mounting holes were restrained with fixed displacement androtation. The Frequency Case was then used as the reference for the restraint


excitation in the Harmonic Dynamic Response cases. Three different HarmonicDynamic Response Cases was used for excitation of the suction strainer in X-, Y-and Z-direction. For each direction a representation was inserted which includedthe accelerations in the respective directions at certain frequencies occurring inthe central of gravity for the suction strainer. The excitation representations arevisualized in figure 5.6. The resulted fatigue stress caused by excitation of thecomponent was captured and analysed, the maximum stress had to be less thanthe fatigue constraint of 6 MPa in order to be considered feasible.

Figure 5.6: The frequency and excitation amplitude in X,Y and Z-direction.

Optimization results

The parametric CAD model was then optimized using Heeds as the solver and CA-TIA analysis for evaluating designs, one example of a feasible design from Heedscan be seen in figure 5.4. The concept did fulfil the constraints regarding mini-mum allowed eigenfrequency for the three modes, it also fulfilled the constraintsregarding fatigue limit when exciting the component according to the excitationspectra in X-, Y- and Z-direction.

However, this concept did not fulfil the requirements regarding manufactura-bility for sand casting since the cross sections varied too much which generateshot spots. Nor was the concept cost efficient since it would have required severalpartition planes for the braces which results in a more expensive manufacturingprocess of the mould. From the Heeds optimization the conclusion was drawnthat the cross sections of the braces were large in order to reach the minimumallowed eigenfrequency and not for the fatigue limit since no design violated theconstraint of 6 MPa. Which implies the concept could be improved to a structureusing less material while still fulfilling the eigenfrequency constraints. The deci-sion was made to redo phase 3 from the beginning, to find a concept fulfilling therequirements for the manufacturing method.

38 Results

Revision of the Parametric model

Based on the findings from evaluation of the first concept, a second concept wasdeveloped to improve the faults. The concept can be seen in figure 5.7 and theconcept was developed by combining the topology optimization in Inspire with theguidelines for sand casting described in chapter 2.5. The new concept had evencross sections for enabling consistent flow and to avoid hot spots. It also used aless complex geometry simplifying the creation of the mould, since the partitionplane can follow one surface. Rather than the previous concept which used bracesat different levels in the space, which makes a more complex mould for ensuringdraft angles at all braces in the partition plane. The new concept did also usethe guideline of strengthening the component by using ribs in the design ratherthan adding material. The design avoided sharp corners and sudden changes inmaterial thickness.

Figure 5.7: The second CATIA developed concept of the suction strainer

The parameters used in the second concept can be seen in figure 5.8. Forexample the material thicknesses were controlled individually for the geometriesto the left and right. The rib thicknesses placed underneath the left and rightgeometry was also changed individually. The parameter values for the design issummarized in table 5.1.


Figure 5.8: Description of the parameters used in the concept

To make sure the CAD-model had parameters enabling a wide range of con-figurations with few errors, a robustness and flexibility analysis was conducted.The robustness for the model was calculated using equation 2.1. The analysiswas performed on 100 different design configurations in the design space using theLatin Hypercube design of experiments technique. Of these 100 designs five failedwhich implies a robustness of 95%.

Optimization results for the second concept

The results for the selected design from the shape optimization in Heeds are sum-marized in table 5.1. The mass of the concept was 0.69 kg and the maximum stresswas 5.6 MPa. The maximum stress occurred when excitating the component inY-direction, the sensitive areas are visualized in figure 5.9. The most sensitivespots were the rib placed above the surface and the rib connecting the suctionpipe and the closest mounting point.

40 Results

Table 5.1: The optimization results for the selected design.

Response ValueMass 0.69 KgFrequency Mode 1 316 HzFrequency Mode 2 400 HzFrequency Mode 3 527 HzVon Mises Stress X-direction 4.6 MPaVon Mises Stress Y-direction 5.6 MPaVon Mises Stress Z-direction 2.3 MPaRib Height 15.36 mmMaterial Thickness R 3.28 mmMaterial Thickness L 3 mmRib Thickness Right 5.04 mmRib Thickness MP12 3 mmRib Thickness Above 3 mmRib Thickness Left 4 mmHole Size L 100 mmHole Size R L 50 mmHole Size R U 50 mm

Figure 5.9: Maximum stress in Y-direction resulted from frequency excitation.


A Pearson correlation matrix of the two most critical responses was created,which which was the first eigenmode and the mass. The figure 5.10 illustrates theparameters which the responses mentioned were most sensitive to. One importantnote from the correlation matrix was the first eigenmodes large sensitive to themass of the suction strainer. Which is a reason why the shape optimization inHeeds reached a lower limit of acceptable mass for not violating the eigenfrequencyconstraint. The frequency constraint was most sensitive to be the rib height, sinceit affects the moment of inertia the most. The mass was most sensitive to thematerial thickness of the left geometry and its corresponding hole size.

Figure 5.10: Correlation matrix design parameters.

5.2.4 Case study-Phase 4The last step in the development process was to verify the optimized concept. Thecalculations used in the optimization framework were simplified which required acomplementing calculation method with greater precision. This method was morecomputational expensive and advanced, the calculation was therefore performed inAbaqus. Instead of using a frequency response as in the optimization frameworka random response analysis was performed to calculate the stresses. The stresseswere compared using root-mean-square von Mises stress (RMISES), the calculatedRMISES should be below 12 MPa, which was based on the fatigue limit of thematerial. The results from the analysis can be seen in table 5.2, the results showedthat the developed component fulfilled the requirements.

From the verification analysis improvements on the developed method wasfound. The improvements to be made on the component was to increase radiussizes in critical places subjected to stress concentrations and fatigue. This kind ofimprovements does not need to be verified again and were therefore implementedon the case study component.

42 Results

Table 5.2: Results from the verification in Abaqus.

Response ValueFrequency Mode 1 [Hz] 314Frequency Mode 2 [Hz] 416Frequency Mode 3 [Hz] 508RMS Mises Stress X-Direction [MPa] 5.5RMS Mises Stress Y-Direction [MPa] 9.7RMS Mises Stress Z-Direction [MPa] 6.6

5.2.5 Case study improvementsThe developed suction strainer was compared with the original component, thiswas conducted to verify the potential of implementing optimization in the develop-ment process. The comparison between the two components can be seen in figure5.11, the developed concept showed that less material in the middle could be usedand still fulfil the requirements.

Figure 5.11: Comparison between the developed and original suction strainer.

When performing frequency- and harmonic dynamic response analysis withthe same set up in CATIA, several conclusions were realized. The results forthe comparison are summarized in table 5.3. Firstly, the weight reduction fromthe original suction strainer to the optimized concept was 53,4 %. The weightreduction gave a negative impact on the three eigenmodes as the table shows. Thereason why this correlation exists can be explained by the theory in chapter 2.4.2,which describes that modes are implicit properties determined by the mass, thestructural stiffness and the damping properties.


Table 5.3: Comparison of the original suction strainer and the developed concept.

Response Developed OriginalMass [Kg] 0.69 1.48Frequency Mode 1 [Hz] 316 347Frequency Mode 2 [Hz] 400 718Frequency Mode 3 [Hz] 527 813Von Mises Stress X-direction [MPa] 4.6 4.01Von Mises Stress Y-direction [MPa] 5.6 1.22Von Mises Stress Z-direction [MPa] 2.3 2.61

44 Results

Chapter 6

Discussion

This chapter discuss the outcome from the thesis and is divided into two parts.First the methodology is discussed, which describes how it may has affected theoutcome. In the second part the main results are discussed.

6.1 Methodology discussionThe developed working method was elaborated in parallel with the case study,which gave a good insight in what steps used when developing the suction strainer.Due to the parallel approach the developed working method was refined iterativelywhen the case study needed changes. This resulted in a relatively general workingmethod for suction strainers and similar products. If the method would have beendeveloped before evaluation on the case study, the method would most likely bemore general and easier to apply to a larger variation of products.

One aspect which could change the method used for the thesis was if thecase study would be a concept development project rather than an improvementproject. Since the suction strainer was relatively pre-defined, the geometricalboundaries were restricted and the design freedom was not as large as in a conceptdevelopment project. Furthermore, since the suction strainer was already underdevelopment the first phase in the developed method was mostly defined regardingthe requirement, goals and disciplines involved etc.

The authors made the decision to exclude interviews in the pre-study. Themain reason behind this decision was judged by the authors that the informationgained from the interviews would not be worth the time required for conductinginterviews and processing the collected data. Note, the judgement was based onseveral factors, one was the authors availability to previous degree project withinthe subject where interviews had been conducted. Where the findings from theinterviews were similar as documented internal standards at Scania which the au-thors used instead. Another reason why official interviews during the pre-studywas not conducted was because the authors preferred to verify with the theoreticalframework instead. The findings and conclusions from internal documents wereverified with the supervisor which can be interpreted as a type of interview, how-

45

46 Discussion

ever not official with forms etc. The interviews would cover what the developmentengineers at Scania thinks about the current working method. Which would be agood base when developing the suggested working method and avoiding flaws inthe current method.

6.2 Result discussionIn this chapter the findings from the developed method is discussed and comparedwith the development method used at Scania. The chapter does also cover adiscussion of the results from the case study with a main focus of the findingsfrom the topology- and shape optimization as well as the design verification.

6.2.1 Developed working methodThe method has been developed to showcase how design optimization and simu-lation driven design can be used. The developed method is in a design engineerspoint of view with the main focus on design of components. Which brings theissue of directly replacing a more general method for all kinds of projects, fromsystem design projects to designing of specific components. Since several stepsare specific within detail design and carries the approach of being able to performoptimization on an investigated component completely. In order for the method tobe used at Scania, for various components, it would probably need a more generalview. However, to be able to answer the research questions the method needed tobe more complete and specific. The method was developed with Scanias currentworking method and Ulrich and Eppingers product development process as sup-port. The biggest similarity between the methods are the sequence of activitieswhich is concept generation followed by detail development and lastly verification.Because of this similarity it enables an easy integration among the methods andintegration for a desired working procedure, meaning a combination of the threemethods will fulfill the product development process. The biggest differences be-tween the three methods are the number of phases from idea to final product andwhich extent and generality each phase has.

In Scanias product development method it is crucial to have a global perspec-tive and a possibility for parallel workflow. These factors have not been taken intoaccount for the developed method since the focus have been design optimizationfrom a design engineers point of view. However, an integration of the design op-timization in the companies development process should not prohibit the globalperspective and parallel workflow.

The method provided in this thesis was developed in parallel with applying iton a case study, the investigated component has most likely affected the methodin a certain way rather keeping a widely applicable method. For instance, Ulrichand Eppingers method have a step called system level design, the step involvesinvestigation of the products architecture and subsystems. This kind of step is notconsidered in the developed method since the case study was already well defined.The step would most likely be involved in the process if several case studies would

6.2 Result discussion 47

be performed, this would however not be time efficient given the time accessibleduring the thesis.

6.2.2 Case study discussionThis chapter discusses the most important findings from the case study. It isdivided according to the main parts from the process, topology optimization, shapeoptimization and design verification.

Topology optimization

The usage of topology optimization in the product development process turnedout to be a good concept generator. The topology optimized result gave valuableinformation in a relatively easy and time-effective way. The method gave inspira-tion to the finalized shape optimized concept, which indicates a product could bedeveloped in a time-effective way and desirable results could still be found.

However, the optimization method requires a correct input in order to reach thedesirable results. The boundary input for the case study was relatively easy, sincethe case study was an improvement of an existing component the functionalitiesand boundaries were already defined. If this was a totally new concept withfew limitations the topology optimization would be more complex, this wouldrequire the designer to first define the functionalities, surrounding componentsand fixed geometries before a material boundary was developed and optimized.The definition of the input can also restrict the exploration of possible designs,if the product has high extent of freedom several concepts proposals needs to bedeveloped with different boundary input data. Since the case study was pre-definedone concept was enough.

The authors had no previous experience of Inspire and topology optimization,the process of developing a working concept was done iteratively. If previousknowledge existed about the process and program the outcome may had beensomewhat differently, more experience would give a better understanding of howthe optimization work.

The case study was only subjected to vibrations which generated a more com-plex problem. In order for the topology optimization to place material load caseshad to be defined. The solution to this was to approximate loads in the non-fixedgeometries. Depending of this approximation the result outcome may differ.

When a design engineer has gained experience the usage of the topology opti-mization can be usable in some cases and less usable in others. If the load case isrelatively straight forward and the component is simple as a bracket for example.Then an experienced engineer can explore and evaluate a component faster with-out topology optimization, by using CATIA analysis and simply cutting materialand see where material is needed. While if a component has a complex geome-try and load case, the topology optimization will perform results an experienceengineer has difficulties to generate.

The design paradox, from chapter 2.1.2, implies changes becomes more trou-blesome and costly when project knowledge increases as the project proceeds.

48 Discussion

When connecting these statements with topology optimization one can say by set-ting up the topology optimization problem does require substantial knowledge ofthe product. When working with product development projects knowledge of thecomponent already exists, since Scania uses their method called lesson learning de-scribed in chapter 4.1.1 to avoid similar problems and transfer knowledge betweenemployees. This results in that design engineers should be able to find key infor-mation when starting the project making big changes with topology optimizationacceptable and cost efficient.

Shape optimization

Overall, the shape optimization did not give a large benefit for the specific casestudy after the topology optimization. The main reason why was because of thenarrow design space from start due to surrounding components. The topologyoptimization gave a very large impact of the result by suggesting an optimizedgeometry based on the design space and load case. The shape optimization wouldhave been more beneficial if the design freedom was larger, giving the optimizationalgorithm more to work with. The case study had the objective of minimizing theweight, which resulted in a small improvement from topology optimization to theshape optimization result. When optimizing a component with multiple objectivescounteracting each other the shape optimization is of much greater interest. Sinceit gives the user the possibility to evaluate the different possibilities and trade-offsfor various disciplines such as CFD, FEM etc.

In order to successfully carry out shape optimization, a robust and flexibleparametric CAD model is required. In order to allow the optimization algorithmto explore a large design space and generate relevant results. Creating a para-metric CAD model with the properties mentioned can be time consuming if theengineer lacks experience or if the component is complex to make parametric. Forexample, if the component has geometric tolerances prohibiting changes in certaindimensions or if the product consists of complex shapes.

Measuring the robustness of the parametric CAD model before carrying outthe shape optimization, is a good verification making sure the CAD model issuitable for shape optimization. Note, the level of robustness is a measurementthe user must be somewhat cautious about. Robustness changes with the designspace, which means a parametric CAD model with poor flexibility, can have a highrobustness if the design space is small. It is therefore better to increase the designspace and if the robustness decreases solve the flaws with the CAD model insteadof decreasing the design space.

When developing the CAD model the manufacturability should be considered,which will lead to abnormalities from the geometry suggested from the topologyoptimization. This was a good learning from the authors who for the first conceptfollowed the topology optimization too strictly, which lead to a concept which wasnot suitable for sand casting. Therefor, the authors defined what requirements thecomponent needed to be manufacturable. Before a new parametric CAD modelwas developed. The extra iteration was not between disciplines but within onediscipline, which would have led to an increase in the duration if it would appearlater in the design process.

6.2 Result discussion 49

For the shape optimization more parameters could have been used to finddesigns with even better performance. The trade-off between perfecting the resultsand the time required applies for every project covering optimization. Since thecase study only had the objective to minimize the weight, the importance of eachparameter was rather obvious. This might not be the case if for instance a problemwith multiple objectives or a complex geometry is considered.

The optimization problem for the suction strainer was relatively straight for-ward, however it requires skill and experience to define optimization problemscorrect. By making the optimization simplified in the beginning, visualization ofthe optimization framework gets easier which might increase the understanding.The need of experience increases with the complexity of the optimization problem.If several disciplines are involved the time duration will increase, making it moreinconvenient when several disciplines need to work in parallel.

The frequency response analysis required the excitation accelerations acting onthe suction strainer which was accessible at Scania. If this would not be the case,simplifications could be made by approximating constant acceleration. However,this would give a worse approximation.

The selected design from the optimization in Heeds did not have performancevalues at the various constraint limits. The reasoning behind the selection was be-cause there will always be imperfections in the manufacturing and the simulations,leading to deviation from the performance in reality. Which might result in a finalproduct with lower performance than the constraint limits. Therefore, selectionof designs not too close to the constraint values are a good safety consideration.

Design verification

The validation phase is fundamental to ensure achievement of product require-ments. The kind of validation and extension will vary depending on type of prod-uct and uncertainties regarding the operation environment. The case study com-ponent was verified with a more accurate frequency calculation model comparedwith the one used in the optimization framework. The reason this verificationwas enough for this component was the previous experience of similar products.Physical testing was neglected due to the scope, but it would verify the componenteven further.

The verification of the design was performed by the authors, the reason beingif an FE-expert was going to perform the calculations it would have taken severalweeks, since the thesis was not prioritized. Therefore, an FE-expert consulted anddirected the calculations with correct method and procedure. The final decisionwas done by the FE-expert but based on the generated results from the authors.This would not affect the trustworthiness of the results.

Depending on the product the calculation model used in the optimizationframework can vary, the type of verification and calculations model need to bedefined in the start-up phase of the project. If the calculation model is accurateenough and experience exist of similar components, the verification step could beexcluded.

50 Discussion

Chapter 7

Conclusion

This chapter summarizes the conclusions from this master thesis, covering thegoals, purposes and answering the research questions stated in the introductionchapter. The goal with the thesis was to develop a working process including designoptimization and simulation driven design to implement at Scania. The methodwas tested on a case study, ensuring the most valuable steps was included tosuccessfully carry out design optimization. The purpose of the thesis was to showthe potential with design optimization in the development process and how it canminimize iterations among disciplines. This was achieved by showing a new designwith a weight reduction of 53,5% compared to the original design with minimumamount of discipline iterations. The weight reduction results in an increase of thecomponents performance and since the weight correlates directly to the materialvolume needed it also results in an improvement regarding economical aspects.

In order to meet a sustainable future continuous development of each com-ponent to maximize performance and minimize weight is needed to minimize theglobal emissions. By reducing the wei

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