Reducing Product Development Risk With Reliability Engineering - Wilde Analysis

7/30/2019 Reducing Product Development Risk With Reliability Engineering - Wilde Analysis

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The following is asummary of

the NAFEMS presentation. We would be pleased to present this information at your offices, focusing on yourareas of interest. Please "contact us:/contact to arrange a meeting.

Reliability toolsrange in complexity

from simple approaches to managing product reliability data and the application of sophisticated simulationmethods on large systems with complex duty cycles.This presentation explores how gathering enough of the right kind of data and applying it in an intelligent way canreduce risk, not only in product design, but also in managing the associated supply chain and in the Whole LifeManagement of products.This is a demonstration of how sparse data gathered from previous or similar products, such as field/warrantyreports, engineering testing data and supplier data sheets, as well as FEA and CFDmodelling, can inform and

positively influence new product design processes from concept stage onwards.

Reliability methods covered include:

FRACAS (Failure Reporting and Corrective Action Systems)FMECA (Failure Mode, Effect and Criticality Analysis)DoE (Design of Experiments)RGA (Reliability Growth Analysis)Life Data Analysis (e.g. Weibull Analysis)RBDs (Reliability Block Diagrams)

No amount of good


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manufacturing cancorrect a poor design, However, poor manufacturing can ruin the best designs. The three principal requirementsfor achieving a reliable product are:

The design must have margin with respect to the stresses it is subjected to during production andoperational use.The production process must be stable and completely documented. Any variations should beconsidered experimental until proven.

There must be an effective feedback and corrective action system which can identify and resolveproblems quickly in engineering, production and in the field.

A structuredreliabilityengineering strategy

should include the following aspects, depending on the application:

Review & ControlDesign ReviewsFRACAS/DRACASSubcontractor Review

Design & AnalysisPart Selection & Control (including de-rating)Computer Aided Engineering Tools (FEA/CFD)FMECA/FTASystem Prediction & Allocation (RBDs)Quality Function Deployment (QFD)Critical Item Analysis

Thermal/Vibration Analysis & ManagementPredicting Effects of Storage, Handling etcLife Data Analysis (e.g. Weibull)

Test & EvaluationDesign-of-ExperimentsReliability Qualification TestingMaintainability Demonstration TestingAccelerated Life TestingProduction Reliability Acceptance TestsReliability Growth Testing


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QFD Phases

Failure Mode,Effect & Criticality

Analysis (FMECA) is a well established process to identify risks but still not implemented optimally in manycompanies. FMECA can be described as:

The study of the potential failures that might occur in any part of a system to determine the probable effect ofeach on all other parts of the system and on probable operational success, the results of which are ranked inorder of seriousness

Ref: BS 4778 (1983) Glossary of terms used in quality assurance


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FMECA is a technique to achieve high reliabilityIt can be a significant amount of workIt is a team activity with an identified person held responsibleIt allows us to identify the vital few from the trivial manyIt is a continuous improvement process and a live document

Typical FMECA

Possible Outcomes of FMECA

-Change design (introduce system redundancy, reconfigure)-Introduce specific tests-Focus quality assurance on key areas-Use alternative materials, components


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-Change operating conditions (e.g. duty cycles to avoid early wear-out/fatigue failures)-Adapt operating procedures (e.g. allowed temperature/load range)-Perform design reviews-Closely monitor problem areas during testing and use-Exclude liability (for specific applications)

Variation is inherent in all manufacturing processes and environments; the same mathematics applies to toleranceclashes in components assembly. However the further into the tails of a distribution, the greater the uncertainty.Extreme values are rare therefore the distribution may be poorly defined.

In engineering we are usually concerned about the behaviour of variation at the extremes high stresses, high/lowtemperatures, high pressure, weak componentsthere is always uncertainty!!

Most engineering distributions are not NORMAL even if they approximate it, the tails are truncated or parts areselected out (e.g. burn-in). However, in critical stress applications e.g. Aircraft or bridges "there must be a factorof safety (say 2) between the maximum expected stress and the lower 3s value of the expected strength.


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A unifying concept in reliability engineering, the bath-tub curve plots the TIME DEPENDANT failure rate sometimes called HAZARD Rate (It is the probability of failing in the next time interval at a certain age given youhave survived to that age).

There are three regions, each region has distinct failure modes Wear-in = Infant Mortality concept ofBurn-in for electronics

The Weibull distribution models each region. Beta is the SHAPE PARAMETER

Design of

Experiments(DoE)

DoE is a statistical tool that aims to maximise insight using minimum resources by systematic testingFollows on naturally from FMECA analysisExperimental observations recorded in predetermined pattern (the design in DoE)Analysis of response of system to changing factors simultaneouslyGoal is usually to find optimum value of chosen factors

To increase outputTo reduce variation


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To reduce costCompare different designsIdentify most important factors affecting performanceCan incorporate Physical Testing and Simulation

Using DoE & FMECA with FEA/CFD

A fast way of generating many different design optionsWithout re-toolingUsing parametric modelsCan do What if scenarios

Can be used in concept stage forwardUnderstand critical features affecting performanceMulti-variable optimisationReduces potential number of prototypesCan explore the boundary of the design space

Useful for developing (Accelerated) test plans

Accelerated lifemodels usually

consist of:

A life distribution at each stress level (from Weibull analysis)A Life-stress relationship (from physics of failure or a statistical model)

Use engineering knowledge to choose a life-stress modelNeed enough data to find our model parameters

Important role for simulation


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Gives early insight into impact of operating environment on product life and can indicate if current

design is fit for purpose.Provides input to appropriate specifications and applicationsMore than just an MTBF number

A reliability block diagram is a graphical representation of how the components/ subsystems of asystem are reliability-wise connected.Blocks represent the components of the system.

Each block has failure and repair characteristics

Lines connect the blocks.The structure of these connections affects the reliability of the system.


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RBD applications to product design:Establishing specification boundariesEstablishing subsystem and component requirements

Design optimisation (architecture and components)Scenario modelling (failure modes, loads, duty cycle, procurement / running costs)Vendor appraisal and design selectionSpares provisioning


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Dependabilitycovers Reliability,Availability, Maintainability and Project Management issues.Dependability assurance requires systematic and rigorous effort throughout the product/process lifecycle andneeds an honest dialogue between the Purchaser, Supplier and End User.

Strategies to reducerisk;

Quantify risk ()Learn new tools for solving old problems

Use Computer Aided Engineering tools as early as possible (even in concept stage)Define the operating environment, mission profile and expected level of reliability (&maintainability) and communicate openly with suppliers.

Tailor processes to critical design objectivesUnderstand and disposition all failures in product development cycle never ignore outliers!Reduce operational stressesReduce production variationFoster a culture of reliability improvement and risk management (in-house and with suppliers)

Documents

Reducing Product Development Risk With Reliability Engineering - Wilde Analysis