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# MPD 575 Design for Reliability - PowerPoint PPT Presentation

MPD 575 Design for Reliability. Jonathan Weaver. DReliability Development History. Originally developed by MPD Cohort 3 team of Julie Earle, Dave Herczeg, and Jim Van Gilder in Fall 2002. Design for Reliability. Why Design for Reliability?.

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### MPD 575Design for Reliability

Jonathan Weaver

• Originally developed by MPD Cohort 3 team of Julie Earle, Dave Herczeg, and Jim Van Gilder in Fall 2002.

• Reliability can make or break the long-term success of a product:

• Too high reliability will cause the product to be too expensive

• Too low reliability will cause warranty and repair costs to be high and therefore market share will be lost

• Reliability is:

• Elimination/avoidance of failure modes/mistakes

• The probability that a product will perform its intended function:

• Under customer operating conditions

• For a specified life

• In a manner that meets or exceeds customer expectations

• A reliable product is robust and mistake-free

• Probability is:

• a measure that describes the chance or likelihood that an event will occur.

• The probability that event (A) occurs is represented by a number between 0 (zero) and 1.

• When P(A) = 0, the event cannot occur.

• When P(A) = 1, the event is certain to occur.

• When P(A) = 0.5, the event is as likely to occur as it is not.

Two types of failure mode:

a) hard = something breaks

Two root causes:

1. lack of robustness (sensitivity to noise factors)

2. mistakes

• Noise Factors are sources of disturbing influences that can disrupt the ideal function, causing error states which lead

to quality problems.

• A population is:

• The entire group to be studied (e.g. all Ford Contours)

• A sample is:

• A subset of a population selected randomly for analysis (e.g., every hundredth Ford Contour off the assembly line)

• % Failure - % of failures in a total population

• MTTF (Mean Time To Failure) - the average time of operation to first failure.

• MTBF (Mean Time Between Failure) - the average time between product failures.

• Repairs Per Thousand (R/1000)

• Bq Life – Life at which q% of the population will fail

• DFR has many aliases:

• Design for Durability

• Design for Robustness

• Design for Useful Life

• DFR should be considered throughout the PD cycle:

• Early - to develop "product concepts" which are well suited for production (i.e., conceptual product design)

• Continually - to ensure that the chosen product concept is implemented through optimal component design

• The shortest route to higher satisfaction is not only through the dealership service department –

it is mainly through keeping customers out of the service department in the first place.

• Customers who report zero problems with their new cars have an owner loyalty rate of 73 percent and dealer loyalty of 42 percent.

• At 4 TGW, loyalty to the company drops by 1/3 to 44%, while loyalty to the dealer drops to zero.

• The average age of a purchased vehicle at the time of replacement is 5.7 years in the U.S. and 4-5 years in Europe.

• The average lifetime of a vehicle before scrap is 12.7 years in the U.S. and 10 years in Europe.

• Develop a Reliability Plan

• Determine Which Reliability Tools are Needed

• Analyze Noise Factors

• Tests for Reliability

• Track Failures and Determine Corrective Actions

• Planning for reliability is just as important as planning for design and manufacturing. Why? To determine:

• useful life of product

• what accelerated life testing to be used

• where to begin

• Reliability must be as close to perfect as possible for the product’s useful life.

• A Reliability Plan helps ensure that product reliability is optimized within the cost and performance constraints of a program and customer requirements.

• How much reliability do you need? Should you accelerate life testing? Where do you even begin?

• Planning for product reliability is just as important as planning for product design and manufacturing.

• The amount of product reliability must be in proportion to a product's usage and warranty goals. Too much reliability and the product will be too expensive. Too little reliability and warranty and repair costs will be high.

• You MUST know where your product's major points of failure are!

• Block Diagram

• P-Diagram

• QFD

• DFMEA & PFMEA

• Design Verification Plan

• Key Life Testing

• Weibull Testing

• Reliability Demonstration Matrix

• Three categories:

• Series

• Parallel (Redundant)

• Complex (combo of the two – shown below)

Noises

Input

Outputs

J

System

Signal

IDEAL Response

(energy related)

(energy related)

error states/

failure modes

L

Control Factors

• Potential Failure Mode

• Potential Effects of Failure

• Severity

• Classification

• Potential Cause/Mechanism of Failure

• Occurrence

• Design Controls (Prevention/Detection)

• Detection

• Risk Priority Number

• Recommended Actions

• Responsibility/Target Completion Date

• Actions

• Test Specification

• Acceptance Criteria

• Test Results

• Design Level

• Quantity Required

• Quantity Tested

• Scheduled Start/ Complete

• Actual Start/ Complete

• Remarks

Robustness Assessment and Noise Factor Management Matrix

“In the development of robustness, it is essential to provide one noise condition for each failure mode”.

Don Clausing, Professor of Engineering, MIT.

Potential Failure modes

Available Tests

Failure mode to test traceability and

Noise factor to test traceability leading to ...

Reliability & Robustness Demonstration

Noises #1

Noise factor management strategy

Noises #2

Noise to failure mode traceability

Noises #3

Noises #4

Noises #5

Robustness Demonstration

Battery Suspension bushing

• Inner Noises

• Wear-out or fatigue

• Piece-to-piece variation

• Interfaces with neighboring subsystems

• Outer Noises

• External Operating Environment (e.g., climate, road conditions, etc.)

• Customer usage / duty cycle

• Change the design concept

• Make basic current design assumptions insensitive to the noises – design out failure

• Parameter Design

• Beef Up Design

• Insert a compensation device

• Disguise the effect - Send the error state/noise where it will do less harm

• 1. 2. 3. 4. 5.

Change (i)Parameter (ii)Beef-up Reduce Comp- Disguise

Concept Design. Design Noise ensate

Piece-to-piece x x x

Wear Out x x x

Customer Use x x x

External Environmentx x x

System Interactions x x x x

• How robust are the products?

• Test to Bogey: assessing performance at a predetermined time, cycle or number of miles. It estimates the proportion of failures at a particular time. pass/fail

• Test to Failure: shows when a component or system can no longer perform at a specified level

• Degradation Testing: focuses on the key stresses associated with real world uses – for example - increasing the tire load to create a tire failure

• How can you shorten the reliability test time for new designs?

• Key Life Test/Accelerated Test

• Proportional Hazard Model to Tire Design Analysis

• Perform Root cause analysis

• Consists of laboratory tests aimed to duplicate field failures

• Tire geometry and physical properties are selected as variables that potentially affect the tire

• Survival data is analyzed by a proportional hazard model

• The adequacy is assessed by the chi-square goodness- of fit test and the Cox-Snell residual analysis

• Identify elements of a tire design that affect the probability of tire failure due to failure mode in question.

• Type of failure mode analyzed – tread and belt separation

• Tread and belt separation can be considered a sequence of two events:

• Failure crack initiation in the wedge area

• Crack propagation between the belts

• Design characteristics that could be variables:

• Tire age

• Wedge gauge

• Interbelt gauge

• End of belt # 2 to buttress

• Peel force

• Percent of carbon black (chemical in rubber)

• Testing procedure

• Dyno testing

• Warm up over 2 hours at 50 mph

• Cool down over 2 hour at full stop

• At 1300 lbs of load: speed steps starting at 75 mph and increasing by 5 mph every half hour till 90 mph and then every hour till failure

• At 1500 lbs of load: all the above speed steps are half-hour duration

• Test speed profile

• Vibration and sound pattern of tire before tread and belt separation failure

• Test data set used in proportional hazard analysis

• Estimates of proportional hazard model with covariates identified

• Estimates of Proportional Hazard Model with statistically significant covariates

• Exponential probability plot of Cox-Snell Residuals

• Cumulative Hazard function predicted from the estimated model based on some typical values of covariates for “poor” and “good” tires

• Conclusion

• Wedge and interbelt gauges as well as the peel force are significant factors affecting hazard rate of tire and belt separation failures in an inversely proportional way

• Agree with hypothesis

• Component design and manufacturing technologies are becoming increasingly complex.

• As geometries shrink and development cycles shorten, opportunities for defects increase.

• Testing for Reliability is becoming increasingly important.

• This process involves:

• Data collection and selection

• Set up databases for tracking failures

• Warranty, Early Warranty, Things Gone Wrong

• Analyzing trends

• Performing closed loop analysis/corrective action

• Calculating observed reliability parameters

• Assessing reliability growth.

• Brake warranty is on track with targets and achieves more than 60% warranty CPU reduction since 1994

• Brake health charts were instituted in 1995 to monitor key performance index and drive design competency

• Supplier business unit reviews (BURS) quarterly to address key quality and manufacturing issues

• Time-to-Failure Curve

• Many CAE models have limited capability to represent real-world noise; therefore, surrogate noise based on engineering knowledge is required.

• Precise reliability estimates require precise knowledge of statistical distributions of noise factors.

• As a contrast, comparative reliability assessments and robust design require only approximate knowledge of statistical distributions.

• Many CAE models are computationally expensive

• preparation time to set up the model

• computing time

• Many CAE models focus on “error states” (e.g., fatigue, vibration, noise); therefore, a multi-objective optimization is often needed.

• In early product development, when the impact of robust design can be greatest, design objectives and constraints are still imprecise.