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Design for Reliability Approach in Magnetic Storage Industry A. Parkhomovsky, R. M. Pelstring Reliability Engineering, Motor Design Division, Seagate Technology.

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Presentation on theme: "Design for Reliability Approach in Magnetic Storage Industry A. Parkhomovsky, R. M. Pelstring Reliability Engineering, Motor Design Division, Seagate Technology."— Presentation transcript:

1 Design for Reliability Approach in Magnetic Storage Industry A. Parkhomovsky, R. M. Pelstring Reliability Engineering, Motor Design Division, Seagate Technology

2 Outline Introduction i.Early Reliability Failure Detection ii.Design for Reliability Approach Reliability Risk Assessment i.FMEA ii.Fault Tree Analysis Predictive Reliability Modeling i.Understanding of physical processes in the product ii.Identification of critical to reliability parameters and possible failure modes iii.Design for Reliability Modeling using DOE and first principles approach iv.Reliability Risk Assessment using predictive models Customized Accelerated Stress Tests Summary

3 Spindle Motor Cross Section Journal Bearing Sleeve Shaft Journal Gap Hub

4 Design for Reliability Definition The tool set that supports product and process design (during the Product Development Cycle) to ensure customer expectations for reliability are fully met. I n it i a l After Current Stressing

5 DFSS vs. DFR DFR focuses on achieving high quality over time and across stress levels. DFSSDFR VOC MSA DOE Control Plans ANOVA QFD FMEA Regression Flowdown Environmental & Usage Conditions Life Data Analysis Physics of Failure Accelerated Life Testing Reliability Growth Warranty Predictions FA recognition General Linear Model Tolerancing Sensitivity Analysis Modeling Hypothesis Testing

6 Identify and Design Optimize FMEA S = ? O = ? E = ? Fault Tree Critical to Reliability Parameters (CTR) and Supplier Capability Reliability Models Statistical Reliability Prediction Design for Reliability Validate Motor Design Limits Testing Concept Verification Control SPC Post- Transfer Control Measures Verify System Margin and Robustness

7 Product Development and Life Cycle Process Design For Reliability Reliability Verification Product and Process Analysis Physics of Failure understanding and modeling FMEA, design risk analysis, Fault Tree Design, process and product analyses Failure Analysis Early Reliability Tests Design Limit Tests Field Data analysis Gap Closure though interrelated concurrent activities

8 Ensuring Reliability in the Product Development Process Concept Evaluation Design Maturity Transition Production Product Development Phases Fault Tree, FMEA, Design Rules Early Reliability Tests Reliability Limit Tests Ongoing Reliability Tests

9 1.Design Out Failure Mechanisms 2.Reduce Variation in Product Strength 3.Reduce Effects of Usage/ Environment 4.Increase Design Margins Utilization of the design, product and process knowledge Design for Reliability Approach Strategies

10 Design for Reliability Implementation Benefits Seagate benefits: Significant Reduction in Cost of development. Increase in the number of orders for disc drives. Reduction in the reserve and storage needs. Customer integration failures reduced. Field failures reduced. Supplier benefits: Larger allocation of business for suppliers commodity. Improved designs and specifications allowing more opportunity for optimization of the supplier’s process. Improved yields with more predictability. Less negative surprises.

11 Best Practices Define Success Reliability must be designed into products and processes, using the best available science-based methods. Knowing how to calculate reliability is important, but knowing how to achieve reliability is equally if not more important. Design for Reliability practices must begin early in the design process and be well integrated into the overall product development cycle.

12 Comparative Resource Commitment Actual Resource Level Post Release Problem Teams Time Planned Resource Level Resource Level Expected Resource Level with Design for Reliability Many Changes Few Changes Shorter Development Cycles Efficient Use of Resources

13 Reliability Model Feedback Loop Product Op-shock 250 g’s 2 ms Mobile Market Requirement Fault Tree Analysis Design opportunity and model gap identified to “break” failure chain. Model Development and Results F Impact  contact Design Improvement Contact relief to reduce contact stress.

14 Fault Tree Model – Shock Failure Fault Tree general skeletons are developed, then they are easily adapted to the particulars of each design.

15 FMEA – Test Linkage: Example Motor Design FMEA ItemPartPotential Failure ModeEffects of FailureSPotential CauseODesign VerificationERPN 11 Sleeve/ Thrust Cup assy excessive wear on thrust surface motor seizure10 High runout, contamination (ECM) 2runout measurement240 15Bearing assembly components rubbing while spinning motor lock up, oil leakage9 parts tolerance allow contact or not meeting print. 4 Min Gap model includes all surface and diameter parameters, bearing drag test will be correlated to journal gap. Performance testing. 136 18Bearing assemblyjournal wear change in performance, oil degradation, motor lock up & oil leak from gyro test 8 wear from operating tests, gyro scopic wear, CSS 5 design validated through testing and run more that 60k cycles 280 23EMEM bias force too high reduced fly height, increased wear rate 5 Misalignment of stator, magnet or bias ring. Incorrect magnetization 2 In-process height measurements, drawings/tolerance studies, magnetization 330  The Design FMEA is developed based on critical failure modes from the fault tree analysis.

16  Reliability tests used are developed to address high risk items in the FMEA.  Design limit variables (e.g. groove depth, coating thickness) are selected based upon failure mode sensitivity.  Acceleration and stress factors (e.g. temperature, load, orientation) are selected based on design knowledge and product environment. Design Limits Test Development

17 Total Failures by Mode – Customer Integration Data represents a < 5 % FA of all Customer Integration Failures 0 5 10 15 20 25 30 35 Failure Mode1 Failure Mode 2 Failure Mode 3 Failure Mode 4 Failure Mode 5 Failure Mode 6 Failure Mode 7 Plan to attack these failure modes in the ORT plan A B C DE QTY Selection of top 5 Field Failure Modes

18 Total Failures by Mode – Field Returns Data represents a < 5 % FA of all Field ARR Failures 0 5 10 15 20 25 30 35 Failure Mode 1Failure Mode 2Failure Mode 3 Failure Mode 4 Failure Mode 5 Plan to attack these failure modes in the ORT plan H G IJ F QTY Failure Mode 6 Failure Mode 7 Selection of top 5 Field Failure Modes

19 Defining Acceleration Factors Acceleration factor (A F )is the ratio of the characteristic life at the use and accelerated test conditions:

20 Multiple Stressor Acceleration Factor Calculation 21total AF  Where: AF 1 is the acceleration factor for stressor 1 AF 2 is the acceleration factor for stressor 2 Life spec – the motor life per specification

21 Typical Stressors Variable Speed profile Time/Number of Cycles Temperature Humidity Operating and non operating shock Electrical bias Load

22 A failure is defined as a significant change in the motor performance parameter over time/cycles. Definition of Failure Parameter

23 Capillary Seal Analysis Meniscus Surface Area Calculation Shock direction Capillary Seal Non-operating Shock Analysis

24 Capillary Seal Fill Process Trade off Gravitational Sag and Shock limited Evaporation limited Model based

25 Capillary Seal Gap Design Trade off Model based Gravitational Sag limited Evaporation limited

26 Oil Sag due to gravity, margin to fill hole

27 27 Autocatalytic Reactions An Autocatalytic reaction is the reaction where the product of the reaction is also a reactant. The approach to an autocatalytic rate equation: The rate of Change in concentration of the component(s) in an autocatalytic reaction and is described through the logistic equation

28 28 Sigmoid Logistic Curve In Case of the oil (ester) hydrolysis which is auto catalyzed by acids: RCO2R’ + H2O → RCO2H + R’OH (a) RCO2R’ + RCO2H + H2O → 2 RCO2H + R’OH (b) The general rate change equation of the autocatalytic reaction:

29 29 Run Current Analysis of the Lubricant Hydrolysis Assume linear dependence between the Irun and the concentration increase of the hydrolysis reaction. Fit the Logistic Curve into the existing Irun versus time equation:

30 Understanding Wear Wear is the erosion of material from a solid surface by the action of another solid. There are four principal wear processes: 1.Adhesive wear 2.Abrasive wear 3.Corrosive wear 4.Surface fatigue Also wear can be classified as dry wear, semi-lubricated wear and lubricated (wet) wear. Wear is a complex phenomenon that is a result of generation of thermal or/and chemical energy. Wear in the bearing is generated as a result of the contact forces acting between the wear couple components. The work of wear can be calculated from the relation below if the spin down profiles and the forces acting on the bearing components are known. We assume that the wear depth is proportional to the contact pressure in place of contact.

31 Low Parameter3 Hi 33 24 28 20 42 30 31 22 26 38 40 44 Hi Parameter2 Low Low Parameter1 Hi Orientation 1 14 12 8 18 3 34 35 19 15 43 29 1 21 27 37 32 10 16 13 11 7 5 Failures are marked in red Induce motor failures by testing beyond customer specifications Responses: Responses: 1. Wear 2. Time to failure Factors: Factors: 1. Parameter 1 2. Parameter 2 3. Parameter 3 Categorical: Categorical: 1. Orientation Stress Tests to induce failures Orientation 2 Low Parameter1 Hi Hi Parameter2 Low Low Parameter3 Hi

32 Typical Wear Rate Wear rate vs. sliding distance Contact (sliding) Distance Wear Rate L Assume that the wear coefficient is a constant (average wear coefficient) for a given material pair to simplify wear experiments.

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36 Critical Parameter Scorecard

37 Summary A successful implementation of Design for Reliability (DFR) approach in high volume spindle motor development and manufacturing demonstrated a significant benefit in identifying and addressing critical failures and accelerating design stages. We have developed, validated and implemented a number of physics and DOE based predictive reliability models to address the design CTQ early in the concept phase. In addition to this, a suite of highly accelerated stress tests was successfully developed to identify critical failure modes in the prototype build stages.


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