DESIGN OF EXPERIMENTS for Reliability. Louis LaVallee Feb 2012

Transcription

1 DESIGN OF EXPERIMENTS for Reliability Louis LaVallee Feb 2012

2 >352 Registrants, 10countries Registration question #1 results Do you have any experience with Design of Experime nts? Yes, we use it occasionally No, we haven't used it but would like to learn more Yes, we used it once 45% 31% 15% Yes, we use it all the time 9%

3 Registration question 2 results Have you ever used DOE during Reliability? No we have never used within any aspect of Reliability 48% Yes we have used for Reliability Testing 24% Yes we have used during Design Analysis 17% Yes we have used for Manufacturing Screening 7% Yes we have used for Material Selection 3%

4 POLLING QUESTION #1 Are there any aspects of DOE you are most interested in? DOE as it relates to model building DOE as it relates to comparisons DOE as it relates to screening out unimportant factors DOE as it relates to best nominal settings All aspects of DOE After signing off the webinar, you will be asked to take a quick 3 minute survey If you fill out survey, you will receive a link to the slides and to the broadcast.

5 Who Are We? Ops A La Carte Santa Clara, CA Founded in 2001 Named top 10 fastest growing, privately-held companies in the Silicon Valley in 2006 and 2009 by the San Jose Business Journal. Over 1350 projects completed in 10 years Over 500 Customers in over 30 countries Over 100 different industries, 6 main verticals Clean Tech, Med Tech, Telecom, Defense, Oil/Gas, Consumer product All are represented in today s audience We conduct FREE monthly webinars.

6 Presenter: Dave Vaughan, National Sales Manager, HALT/HASS Systems HQ in Minnesota World s Largest Cryogenic Equipment Supplier Building quality products since 63 HALT/HASS chamber manufacturer since 97 ISO 9001 Certified Only HALT/HASS supplier that can offer you a true Turn-key solution with Tank, Pipe and Chamber. Hundreds of HALT/HASS chambers worldwide.

7 Ops A La Carte is a Professional Reliability Engineering firm focused on providing you confidence in reliability throughout your product life cycle. Mike Silverman Managing Partner Offices & labs (408) mikes@opsalacarte.com Louis LaVallee Sr. Reliability Consultant NY Loul@opsalacarte.com Website

8 At the end of the module, you will be able to: Describe the basic concepts of experimental design and connections to reliability tests Describe the value of using experiment design concepts and practices over current practices List the process steps in experimental design applications State how experimental design with reliability methods impacts product / process design

9 Fair Comparison of competing Teams Single Factor is team and there are two levels Team is a nominal categorical factor. Levels have no inherent ordering implied. Primary Objective: Select Best Team Primary Response: Final point score Secondary responses: Points by quarter Offensive Yards gained Turnovers, intercepts, Called a Multivariate Response.

10 Football/DOE Terminology Blocking Changing ends at halftime is to assure no bias in results due to sun, wind directions, shadows, etc Replication Playing same team twice in the same season Repetition Playing same team twice in a row 1-FAT Single factor design, e.g. team Nested factor Each team s play book., i.e. Team 1 has 30 plays, and Team 2 has 30 different plays Balance Each team has equal number of players on the field at any given time Random effect Fair Coin Flip to start game Strength Important property of orthogonality of columns

11 DOE & DFR Backdrop Design of experiments (DOE) and design for reliability (DFR) are both team based activities. Both DOE and DFR need an overall proactive strategy for integration of product quality and reliability development. Both DOE and DFR require addressing most difficult and time consuming activities early on. Both DOE and DFR thrive in a climate of results-driven activities (not just data-driven) with solid metrics to quantify them.

12 Are there ways to improve reliability without having to measure reliability? Build an analytic model (e.g. Weibull, Arrhenius) to describe expected behavior, to predict reliability. Accelerate failure occurrence with stress variables, while still being able to estimate reliability under nominal conditions Take advantage of DOE efficiencies (for considering many factors in concert ) and to consider design optimization objective. Build a physical model (Physics-of-failure approach) understanding the failure mechanism, and root-cause failure processes to predict reliability under usage conditions.

13 Are there ways to improve reliability without necessarily having to measure reliability? Focus on stability of Input-Output functions early, i.e. reduce functional variation to improve reliability. Parameter design, tolerance design, control system design, calibration methods, prognostics, measurement systems, Consider tendency to fail rather than time-to-fail, as in accelerated degradation testing. Apply multiple noise factor stress levels that accelerate failure or tendency-to-fail.

14 Cooking Popcorn Experiment 7 Control 2 levels Factorial (all combinations) 2 7 =128 Factor Level 1 Level 2 Oil Type Canola Peanut Oil amount 2 tbsp 4 tbsp Burner setting hot (8) Very hot (10) Popcorn kernels Snowflake Mushroom Motion during cooking None Agitate Storage condition Ambient High RH Pot Material Aluminum Copper

15 Popcorn Measures After the fact measures Count popped & unpopped kernels and estimate fraction defective Weigh popped & unpopped kernels and estimate weight fraction defective Count number of defective kernels (charred, partially popped, hulls intact) Taste, smell, texture categorical data Kernel final Volume, density During cooking Time to first & last pop (hull rupture) Hull time-to-fail distribution x, Hull Pressure (t) Kernel Internal Temperature (t) Long after cooking Revenue from selling popcorn Count of broken teeth & dental bills OK/ Not OK Measures are chosen, not given!

16 Noise Factor Definitions: Factor that disturbs the function, which is difficult to control, or too expensive to control. External noise factors include temperature, humidity, dust, vibration, contaminants, Unit-to-unit variation from a manufacturing process. Deterioration, as time passes. Selection of noise factors and levels is a prediction of how device will be used downstream

17 DOE for Noise Factor Identification (screening experiment ) Objective: Determine most important external noise factors affecting performance using experimental design Noise factors for future manufacturing, assembly, transportation, field operation, service, recycling, end of life Temperature changes everything, so it makes a favorite noise factor. Vibration and shock also fairly common noise factors

18 DOE for External Noise Factors Screening Experiment Predict which external noise factors e.g. Temp, RH, vibration represent the largest design vulnerabilities. Levels of noise factors are selected (and fixed) to create response dispersion similar to predicted wide ranging usage conditions. Assign noise factors to orthogonal test plan and implement the plan. Determine contribution rates and use information to create further test plans. From test results, identify most important factors and direction of disturbance for each factor. Consider noise factor compounding going forward.

19 Response Marginal Means Plot from L 9 Experiment Data 4 3 levels 15% 45% 5% 15% Mean A B C D Factors Combination A 2 B 2 C 2 D 2 ~ Mean A 3 B 1 C 3 D 3 = max response A 1 B 3 C 1 D 1 = min response Error % ~20%

20 6-pitch belt or Example: Photoreceptor Belt on Tri-roller assembly Belt tracking and surface velocity control systems active

21 Build flexible technology fixtures early Adjustable Tri-roller Assembly to flex belt sections over small diameter rollers Stationary Flexible Belt section Tension Tension Roll diameter (smaller than actual to elevate mechanical stress) Adjustable rotational speed Adjustable roller penetration Adjustable sample tension Chamber temp and RH

22 Material Improvement Work Process Team selected eight device material & fabrication control factors from Ishikawa diagram and two standard L 9 orthogonal array layouts, (considering four factors at a time). Problem was cracking life Interval censoring was used. Each experiment included the following noise factors: flexural cycles, spatial location of measurement, operator, others Smaller roller diameter was designed and used to accelerate crazing and cracking on the top surface of the films. Modifications to the materials & process were subsequently verified

23 Tops surface Crazing (precursor to cracking) Tendency-to-fail consideration

24 Complete data

25 Polling Question When is the first time you consider design reliability issues in your product delivery process 1) Concept generation & reuse of existing designs 2) New Concept selection 3) Detailed Design for subsystems 4) System integration & Internal Product testing 5) Field trials

26 Orthogonal Array Selection Beginners should use standard orthogonal arrays like L 4, L 8, L 9, L 12,L 16 L 18 Usually just enough degrees of freedom. Objective needs to be considered in selection. Intermediate practitioners should use more customized arrays which pivot off standard arrays using column merging techniques, dummy level assignments, compound factor assignments, Advanced practitioners can make more elaborate modifications of standard arrays with linear graphs & algorithms. One can find thousands of OA designs from internet files*, or the use of combinatoric algorithms

27 DOE & DFR throughout Concurrent Engineering Input Design Concept Mfg Process Concept Parameter Design Optimization Mfg process Parameter Design Opt. System Verification Test Process Mfg Process Verification Test Process Tolerance Design Process Mfg tolerance design process Production/ Field Readiness tests On line QC Process Output Robust Product/Process Capable

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29 Experimental Designs Full Factorial Fractional Factorial Plackett - Burman Response Surface Design (CCD) D-Optimal, A-Optimal Mixture Designs Split-Plot Hadamard Matrix Supersaturated Clinical Trials Near orthogonal arrays Box-Behnkin Box-Behnkin 3-level design no vertices

30 Electrolytic Capacitor Ishikawa Diagram

31 Formulating the Experimental Plan: Identify constraints of cost ($) and time to conduct the experiment (Gantt Cart, Cumulative costs) Identify the objective(s) of the expt using multifunctional team Select measurements & understand errors of measurement. Validate measurement systems. Know what you will be doing with the data collected. Do what you can do today! Don t wait for every element of the plan to be ready. It is better to make early progress. While first experiment is running, preparation for follow-on experiments should be underway.

32 Accelerated Life Tests (ALT) Usually Fixed Design, Various Patterns of Noise Factors ALT Qualitative ALT Quantitative ALT HALT HASS CSALT SSALT CISALT Single Stress Variable Two Stress Variables Multiple Stress Variables Censored Data No Control Factors! Complete Data More Complex Life-stress Models

33 Quantitative Accelerated Life Tests one stress factor, three patterns Stress S 0 Constant stress ALT Step stress ALT Continuously Increasing ALT Time Time Time

34 Highly Accelerated Life Test (HALT) In HALT, a product is introduced to progressively higher stress levels in order to quickly uncover design weaknesses, thereby increasing the operating margins of the product, translating to higher reliability. Example: electronic circuit board Single noise factor - Temperature Multiple levels staircase (up or down) with specified dwells times & ramp rates. Responses limiting failure modes & stresses at which failures occur, Analysis- Root cause analysis, design fix, and verify improvements

35 HALT, How It Works Fundamental Technological Limit 2008 Ops A La Carte 35

36 HALT, Why It Works Classic S-N Diagram (stress vs. number of cycles) Point at which failures become non-relevant S 2 S 0 = Normal Stress conditions N 0 = Projected Normal Life S 1 S 0 N Ops A La Carte N 1 N 0

37 Margin Improvement Process Lower Destruct Limit Lower Oper. Limit Product Operational Specs Upper Oper. Limit Upper Destruct Limit Stress 2008 Ops A La Carte

38 Margin Improvement Process Lower Destruct Limit Lower Oper. Limit Product Operational Specs Upper Oper. Limit Upper Destruct Limit Destruct Margin Operating Margin Stress 2008 Ops A La Carte

39 F Normal Operating Window Method Paper Feeder Paper Stack misfeed 100 multifeed Fraction Feeds Failing LD 50 0 Normal Force

40 Operating window Method: paper feeder Response: Drive roll normal force LD50 Procedure: Initially set normal force at nominal setting. Feed multiple (~100) sheets of paper. Adjust normal force higher in equal steps until estimated 50% of sheets fail for multifeed condition. Reset force to nominal and adjust normal force lower in equal steps until ~50% of sheets fail for misfeed condition. LD 50 condition is determined for both failure modes. Normal force difference represents positive (or negative) operating window for univariate factor normal force. Control factors like roll material, geometry, speed, timing are investigated to widen operating window. Noise factors like paper type, basis weight, are investigated. Set normal force to balance costs on both sides

41 Constant Stress Monotonic Degradation Pattern Response Easily transformed for linearity, % change Sample 1 Sample 2 D fail 0,0 T 1 T 2 Cycles

42 Accelerated Degradation Test Two common assumptions for degradation data modeling : 1. A parameter D, that can be measured over time, drifts monotonically (upwards, or downwards) towards a specified critical value D fail. When it reaches D fail, failure occurs. 2. The drift, measured in terms of D, is linear* over time with a slope (or rate of degradation) R, that depends on the relevant stress the unit is operating under and also the (random) characteristics of the unit being measured. 3. Readings for each unit are taken at the same time points * Transformation may be needed for approximate linearity

43 Constant Stress Degradation Data Response Degradations rates R, Predicted failures t R 2 R 3 R 4 R 1 D fail t 4 t 2 t 1 t 3 time % change is sometimes used

44 L 8 Degradation Experiment Layout Rates T fail R 1 t 1 R 8 t 8 Minimizing degradation rate and maximize failure time objective

45 Design of experiments can be used to inspect engineering knowledge of a design i.e. to determine the readiness of the design for downstream usage. Assigning critical factors (control, noise, and signal) to an experimental design layout and being able to demonstrate that the knowledge is sufficient to manage the design downstream, even when other factors change.

46 Variation Management is the Key after Before optimization LSL target USL Identification of best nominal values of design parameters to reduce variance and put output on target (with minimal cost increase). Reliability improvement though variation improvement

47 Alternate Plan Layouts L 18 L 18 x N 2 L 18 x N 2 X M 3 1-way layout 2-way layout 3-way layout N 1 N 2 M 1 M 2 M 3 M 1 M 2 M 3 L 18 OA 8 control factors 3-way layout

48 Transformability & Robustness Improvement Response Response N 1 N 1 N 2 N 2 0,0 M signal 0,0 M signal Minimizing the effects of noise factors on transformation of input to output. Improves reliability. Sensitivity increase (tuning) can be used for power reduction, which also improves reliability

49 Mechanical Stress/Strength Interference Theory Probabilistic STRESS Distribution Probabilistic STRENGTH Distributions probability Response Overlap Failure region

50 Minute Accumulation Analysis N=5 samples/expt Inspection intervals # samples still alive A 1 total= 80 from (first 4 rows data) A 2 total= 75 from (second 4 rows data)

51 Survival Probability Curves B 1 B 2 Censored data, ANOVA, Confidence limits, Prediction, Verification tests, Distribution Free, easily understood, accelerated test

52 2-way layout of design factors against HALT profile for noise Responses: Service costs, labor costs to repair, categorical data, fraction of time in operation,

53 expt L 9 Life Test Data Nonrepairable Device Suspended Trial # Interval censored 0,0 Time to failure hrs

54 Missing data imputation: Method of sequential approximation: Use the treatment combinations that have valid available data to estimate right censored data, assuming main effects model of the response. Continue to iterate until convergence observed. For example, the previous slide shows a suspension for treatment combination 6 in the L 9. Initially impute the suspension time and predict what treatment combination treatment # 6 would be, using a significant main effects superposition model. Impute the new value and continue process until solution converges.

55 Summary DOE methods combined with reliability engineering make a powerful combination for product development. Product reliability testing used to take too long and cost too much. Significant improvements have been made in recent years through a combination of experimental design methods, accelerated test methods, physical models development, and measurement technology. Design of experiments provides a variety of tools for obtaining fair comparison of alternatives, and satisfying many different product development objectives.

56 Upcoming Events we are at FREE Quality Event Feb 7, 2012, San Jose Seminar on Gaining Control over Cost and Quality in Global Manufacturing State-of-the-Art Approaches from Design to Sourcing to Production MD&M West Feb 13-16, 2012, Anaheim, CA We will be giving a 1 day seminar called Medical Reliability Testing on Dec 16 from 9-4pm IPC APEX Feb 28-Mar 1, 2012, San Diego, CA On Sunday Feb 26, we will be presenting a 3 hour seminar on Reliability Program Planning: Optimizing Analysis and Testing in a New Product Development Environment.

57 Next Month s FREE Webinar TRIBOLOGY and RELIABILITY FREE WEBINAR on Tribology and Reliability, March 7, 2012, 11:30am-12:30pm Tribology is the science and engineering of interacting surfaces in relative motion. In this seminar, we will introduce the basics of Tribology, their impact on reliability and do so from the perspective of machine applications. Lubrication, film thickness, loads and Hertzian contact types effect the reliability of a design. Weibull is frequently used to model wear characteristics, but is this always the best distribution for characterizing the reliability of machine elements? All of these areas will be explored.

58 Audience questions? Thanks for your attention Further questions? Send us an or call. Results posted on BLOG for further discussion. Link to slides will be sent with survey completion