Interactive Manipulation of Target Functions for the Optimizationof Mold Temperature Control Systems

DIRK BIERMANNTechnische Universitat DortmundInstitute of Machining Technology

Baroper Straße 301GERMANY

biermann@isf.de

RAFFAEL JOLIETTechnische Universitat DortmundInstitute of Machining Technology

Baroper Straße 301GERMANYjoliet@isf.de

THOMAS MICHELITSCHTechnische Universitat DortmundInstitute of Machining Technology

Baroper Straße 301GERMANY

michelitsch@isf.de

Abstract: Designing mold temperature control systems for production tools is mostly done manually by experi-enced engineers using standard CAD software. In contrast to this, several experiments with automation systems tosupport the mold designer have been performed. For hot stamping tools and molds for die casting and plastic in-jection, software systems have been developed to create better or even optimal temperature control systems. Whileadequate simulation methods are mainly a question of processing power, the definition of goals, models, and tar-get functions depends on human computer interaction as the designer has to formalize his knowledge. This paperpresents an interactive software tool to define attributes on mold surfaces in order to define cooling demands for theoptimization of mold temperature control systems. Different experiments are performed to show the effectivenessand to give an outlook on the wide range of possibilities of this new approach.

Key-Words: Die Casting, Optimization, Mold Temperature Control, Software, Computer Aided Design, ComputerAided Engineering

1 IntroductionThe design of cooling solutions for mass-productioncasting tools has a massive impact on the productiv-ity of the final process. To control the temperature ofa tool, a cooling system of connected bores is drilledthrough the tool. The position of bores and their in-terconnection are subject to optimization during molddesign since cycle time, tool life, and product qual-ity are highly influenced by absolute tool temperaturesand internal heat gradients.

Apart from research projects, molding tools aredesigned manually using standard CAD software.This requires profound expert knowledge, but a highpotential for further optimizations can be assumed.To overcome human limitations like simple symmet-ric arrangements or common but suboptimal layouts,an automated temperature control system design andoptimization can be performed using a software tool.The tool can check restrictions with modeling preci-sion and the complexity is almost unlimited.

In this paper we present a new approach to manip-ulate surface attributes of molds to interactively createnew target functions for the optimization of MTCS. Anew software tool for mold designers visualizes dis-cretized values of heat distributions on the surface ofmold geometries. These values can be manipulatedinteractively and represent a new input for the opti-

mization process. In this way, the optimization canbe guided iteratively to create MTCS layouts that sat-isfy the designer’s restrictions and are at least locallyoptimal with respect to the model and the simulationmethod. Depending on the given target function, theoptimization can be influenced in different ways.

In the following, a brief survey of existing at-tempts to the subject is given and existing problemsare described. Next, the software tool is introducedand its abilities are exemplified using various imple-mented features. Finally, experimental results are pre-sented and an outlook on further applications of thenew method is given.

2 Design of Mold Temperature Con-trol Systems

Typically, an experienced engineer has to design ap-plicable temperature control systems for molds afterall other components are already positioned. Creatingthe tempering system is an error-prone task, and veryoften conflicting objectives lead to suboptimal results.Thus, using automated systems taking care of givenconstraints is recommended. A benefit of a computer-aided design process is the ability to use simulationsto predict, e. g., the heat distribution with respect to

Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 239 ISBN: 978-960-474-220-2

the cooling system and, furthermore, to optimize thefinal operation of the mold by changing the design ofthe MTCS.

Different attempts have been made to design cool-ing systems automatically. Promising results havebeen produced by using evolutionary algorithms (EA)to create mold temperature control systems (MTCS)for different bulk production processes, namely hotstamping, die casting, and injection molding [6, 8,11]. Most of them make use of single- and multi-objective evolutionary algorithms [2, 4] to generatenew and optimize existing MTCS designs. For this,one or more objective functions are needed to definea relation on the set of individuals and find a mini-mum/maximum. Examples of target functions are theaverage surface temperature of the mold and the max-imum deviation of local temperatures from the meantemperature. While utilizing the first one results inlow cycle times of the process, the second one leads tominimized shape distortion caused by inner stresses.Other objectives like production costs and constraintsthat must be met, like minimum distances or othertechnical restrictions, also have to be taken into ac-count. Starting with random initial designs, the EAmodifies the position of control points iteratively andcompares existing solutions with new ones that arecreated by mutation and recombination. Keeping thebest ones with higher probability (selection), succes-sively better solutions are generated. Finally, a goodlocal or even the global optimum is found that givesthe highest ranking with respect to the given objectivefunctions.

Many attempts have been made to solve differ-ent problems that are caused by characteristics of themold geometry, design constraints, and the optimiza-tion process [7, 3]. All approaches assume that givenconstraints and measures for acceptable results areknown, but there are two main problems to solve. Foran automated design system that should create optimalresults, rules and models must be defined by experi-enced engineers. These mathematical definitions areformalizations of the knowledge of the mold designerto guide the optimization into the right direction. Thesecond problem is the completeness of models for thesimulation. For performance reasons and in the ab-sence of detailed knowledge, it is impossible to createa complete detailed model, which respects every de-tail affecting the molding process.

For the simulation of the thermal influence of thecooling system, a fast simulation method has beenpresented, which approximates a heat distribution onthe mold [10]. As described before, this distribu-tion can be accumulated uniformly into a single tar-get value expressing some abstract quality of the re-sult. For this, heat distributions of simulations without

Figure 1: Screenshot of MoldPaint with activated clip ed-itor and its effect on the visualization of the model. Addi-tionally, the manipulation is restricted to the visible area.

cooling channels can be used to estimate the coolingdemand, which requires time consuming simulations.Until now, an experienced design engineer had littlepossibilities to guide the optimization process usinghis knowledge. With the interface, which is describedin the next sections, it is possible to make use of amold designer’s knowledge to skip time-consumingsimulations and to readjust the target functions for theoptimization of MTCS.

3 Interactive Definition of AttributesThe key to acceptable optimization results is the def-inition of suitable target functions. Given a completeand detailed model, a widespread simulation of manyphysical effects like temperatures, stresses, flow ofmaterial, and solidification can be realized. However,an optimization using such a broad simulation wouldlead to unacceptable runtimes. The current alternativeis a rough estimate of the effects of cooling systems,but the formulation of constraints and the definition oftarget functions is still a question of research.

To face this problem, a new software tool hasbeen created for engineers to guide the optimiza-tion by manipulating the desired heat distribution onthe surface of the mold. Using this in an iterativeoptimization loop, optimized solutions can be pre-sented to the designer, which can be manipulated andare taken as a new target function for the next opti-mization iteration. A basic requirement for realizingthis is a real-time visualization of surface attributes.Namely, a framework and a front end for engineershas been created for the interactive manipulation ofthree-dimensional geometric models.

Fig. 1 presents a screenshot of MoldPaint show-ing different manipulation tools and their settings onthe left hand side. The right hand side shows a vi-sualization of the model and further assisting views.

Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 240 ISBN: 978-960-474-220-2

Figure 2: On the left-hand side a matching copy and pasteoperation (rotated by 72 ◦) is presented. The selection isoutlined. The right-hand side shows a flat-shaded view ofa mismatching copy operation (mirrored at yz-plane).

Figure 3: Symmetric synchronous editing with smooth tooland different initial states. (a) Initial state with symmetryplane. (b) Simultaneous cursor. (c) - (e) Continuous editingwith different starting values and predefined end value.

The scale depicts the meaning of different colors (ver-tically) and shades (horizontally) to allow an interpre-tation of the visualization. The clip editor managesthe clipping of the viewing volume to see and manip-ulate hidden surfaces. Fig. 1 displays the activatedstate of two clip planes and their effect. A small setof further tools and functions, allowing intuitive andfeasible manipulation of surface values, is describedin the following section.

3.1 MoldPaintThe attributes of each triangle in a set are manipulatedindependently, to avoid the dependency on topologicalinformation. STL-files are imported to define the ge-ometric model. Starting with overall unattributed sur-faces or with initial data from, e. g., a simulation, anydesired heat distribution on the surface can be defined.For this, different versatile painting tools are providedand various assisting functions for, e. g., symmetricshapes are integrated.

Fig. 2 illustrates the application of the copy tool tocopy the attributes of selected triangles and paste them

(a) (b) (c)

Figure 4: Scattered data interpolation. (a): Partially de-fined surface attributes. (b) and (c): Interpolated valueswith power value p = 1 and p = 5.

to other parts of the geometric structure. For this,an interactive and a numerical transformation methodare provided. For partially symmetric parts, a moreadvanced painting mode is available. Fig. 3 showsthe symmetry function that is usable with all pencils.Thereby, multiple parts of the model can be editedsynchronously but with respect to their individual pre-vious state. Depending on various cascadable primi-tive transformations, such as mirroring, translation, orrotation, the cursor is multiplied and areal-time ma-nipulation of multiple areas of the geometric modelbecomes possible.

The interpolation of mesh attributes for the scat-tered data interpolation is another important feature[1]. An adaptation of Shepard’s method based on in-verse distance weighting is used to compute interpo-lated values for each location on the surface. The maincontrol value for this operation is the power value p.Fig. 4(a) shows some initial surface definitions andFig. 4(b) and 4(c) display interpolation results withdifferent power values (p = 1 and p = 5). Tak-ing adjacencies of triangles into account, interpola-tion methods respecting the real distance along thesurface can be implemented as well. Using a volumet-ric model, further interpolation methods can be imple-mented to enhance the quality of the interpolation.

In addition to the temperature as the main surfaceattribute, a second attribute can be manipulated simul-taneously. Visualized by different shades of colors,the second attribute can be set for each point on thesurface. It can be used to weight the matching be-tween defined and simulated temperatures to controlthe penalty of deviations for the optimization. Butother local parameters for target functions are possi-ble as well.

In an iterative design/optimization process, speci-fication data from MoldPaint can be exported and usedas input values for optimization runs. Results for asimple test part are given in section 4, where a targetfunction is used for the optimization that is based onuser-defined surface temperatures. In addition to this,resulting data from ,e. g., FEM simulations can be im-

Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 241 ISBN: 978-960-474-220-2

(a) Reference values. (b) Edited copy of referencevalues as current values.

(c) Differences of current and reference values.

Figure 5: Reference view: extra reference values can beloaded, saved, and copied from/to current values. Differentview modes make it easy to compare current with referencevalues.

ported. Imported values can be used in two differentmodes: The values can be taken as current, editablevalues or they can be regarded as reference to visual-ize differences between current and reference values.

Fig. 5(a) shows reference values that were loadedfrom a snapshot file created earlier. These values havebeen copied to the current surface and then edited(Fig. 5(b)). The given reference values can be dis-played separately in the clip editor or in the mainview. Additionally, special views can be activatedto show the difference between current and referencevalues (Fig. 5(c)). All in all, a variety of advancedview modes have been implemented to support the de-signer’s task.

3.2 PerformanceThe key factor for an effective workflow is the result-ing performance of the interactive user interface. Inthe current implementation not only the visualizationis realized using graphics hardware, but the manipu-

Graphics HardwareTriangles (×1, 000)

20 60 380 830NVIDIA Quadro NVS 295 41 29 7 4NVIDIA GeForce GT 240 ≥ 60∗ 55 19 10NVIDIA Quadro FX 550 ≥ 60∗ 56 34 25NVIDIA Quadro FX 1800 ≥ 60∗ 60∗ 38 26

Table 1: Performance rating for MoldPaint in frames persecond. The values are highly dependent on implementa-tion details and represent only the magnitude of the perfor-mance to prove the practicability of MoldPaint on middle-class graphics hardware.∗Maximum monitor frequency reached

lation and other time consuming operations like inter-polation and copying also depend on OpenGL [5]. Incontrast to the visualization, the manipulation scalesvery well with the number of triangles of the surface.Since each triangle is mapped to one pixel of a con-stant number of texture maps, even meshes with onemillion faces can be manipulated up to 5,000 times persecond on a GeForce GT 240 graphics hardware usingGPGPU (General Purpose Computation on GraphicsProcessing Unit) [5]. Thus, the speed of visualizationlimits the complexity of meshes on a specific graphicshardware, which can be displayed properly.

Table 1 depicts performance measures for a se-lection of graphics adapters including low-cost andhigh-end hardware. The values are only rough es-timates and all non-GPU influencing factors are ig-nored. Thus, they can give only a small insightinto the usability of MoldPaint with complex meshes.Overall, it can be stated that even on low-cost graph-ics hardware like the GT 240 adequate performancefor high-resolution meshes can be accomplished.

4 ExperimentsIn sections 3.1 and 3.2, various methods and possibil-ities of the new software tool are presented. The defi-nition of local tempering demands using MoldPaint isthe first testing scenario to demonstrate how simula-tions can be replaced.

Given an initial tempering demand, the optimiza-tion system presented in [10] is able to predict theresulting heat distribution for any given MTCS andto compute optimized layouts for given target func-tions. For this, the result of a FEM simulation, whichdoes not take any temperature control system into ac-count, can be used to define the initial cooling de-mand. Though simulation-based target functions likethese generate more adequate results, such a simula-tion is a time-consuming operation.

For the estimation of tempering demands in cast-

Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 242 ISBN: 978-960-474-220-2

Figure 6: Result of geometric analysis.

Figure 7: Manually post-processed tempering demand.

ing molds, a simple method has been proposed earlier[9]. The analysis of the geometric model of the work-piece with volume M calculates for every point P onthe cavity surface a spherical neighborhood S and de-termines the ratio of volumes S ∩ M and S. Sincethe ratio relates to concave (low) and convex (high)parts of the mold, an approximation of the estimatedtempering demand of the mold can be computed. Toenhance the results and to compensate modeling inac-curacies, MoldPaint is used to edit the given values.

Fig. 6 shows the model of a part that is typicallyproduced using injection molding. The sprue on theright hand side and other elements of the mold suchas ejectors, ventilation system, etc., are not visualized.The color corresponds to the result of the geometricanalysis without any relation to real temperatures. The

result of the manual post-processing is presented inFig. 7. On the one hand, a simple heat gradient alongthe direction of material flow has been overlaid and,on the other hand, the values at the inner of the moldhave been increased.

The oversimplification of this procedure can bejustified with the given optimization method. Sincethe simulation of the tempering effect of cooling chan-nels is independent of absolute temperatures, a rela-tion between local tempering demands is sufficient.Considering a minimum deviation target function, theEA places the cooling system close to hot surface ar-eas. Referring to the given workflow, the design engi-neer has an indirect control on the optimization result.

Figure 8: MTCS optimized with respect to the temperingdemand defined with MoldPaint. The estimated tempera-tures are displayed gray-scale coded.

Fig. 8 presents the optimized temperature con-trol system and its effect on the heat distribution onthe mold surface. The user has provided the temper-ing demand (utilizing MoldPaint) and additionally asimple initial design of the MTCS. A fast simulationapproach is utilized to estimate the resulting coolingeffect, which is then weighted by the tempering de-mand. Taking the intensity and homogeneity of thesevalues into account, the MTCS is optimized by theEA. The resulting design consists of seven circuitswith 27 cooling segments. Although the mold is freeof undercuts and comes with sufficient draft angles, itis constructed as a segmented mold (segmentation notdisplayed) due to the steep grooves. Since some of thebores have to be placed at locations which are difficultto access, the cavity is partially penetrated and its sur-

Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 243 ISBN: 978-960-474-220-2

face has therefore to be refurbished locally afterwards.Finally, it can be stated that the effect of chang-

ing the tempering demand on the optimization is pre-dictable if the target function is known. Thus, amold designer can guide the optimization by using hisknowledge, but with the advantage of the accuracy ofan automatic design tool to generate optimal MTCS.

5 ConclusionThe results of the previous section 4 show the flowof information and the role of the new software tool.The replacement of simulation runs has been de-scribed and the influence of the defined values hasbeen shown. Only some of the possible features havebeen described and various other applications can berealized. Simple extensions to the given example areother target functions, using the given values in differ-ent ways. Moreover, the interaction can easily be ex-tended to an iterative workflow reading back surfacevalues from the optimization software. An interac-tive control of the optimization will become possibleif a shared user interface is created. This could be thefirst step to new control methods for the presented andother optimization tools.

Acknowledgments: The research was supported bythe Deutsche Forschungsgemeinschaft (DFG) as partof the Collaborative Research Center ‘ComputationalIntelligence’ (SFB 531).

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Proceedings of the 2nd International Conference on Manufacturing Engineering, Quality and Production Systems

ISSN: 1792-4693 244 ISBN: 978-960-474-220-2