IDENTIFYING WORST CASE DESIGNS THROUGH A COMBINED STRATEGY OF COMPUTATIONAL MODELING, STATISTICAL METHODS, AND BENCH TOP TESTS

Transcription

1 IDENTIFYING WORST CASE DESIGNS THROUGH A COMBINED STRATEGY OF COMPUTATIONAL MODELING, STATISTICAL METHODS, AND BENCH TOP TESTS Payman Afshari Andrew Dooris Pat Fatyol DePuy Synthes Spine, a Johnson and Johnson Company V&V 40 Technical Symposium May 7 th 2014

2 Objective To identify the worst performing Interbody Fusion device, AKA a cage, under axial compression loading to mitigate patient risk and support regulatory filing. 2

3 Interbody Cage Is a prosthesis used in spinal fusion Placed between the vertebral bodies. maintains separation of vertebrae and stabilizes it to allow fusion. Cages come in many heights, widths, lengths and angles to accommodate patient anatomy and affected spinal level. The combinations generate more than 400 cage design configurations. 3

4 The 510(K) Regulatory Requirements Axial Compression characteristics of a cage is one of the key markers of its performance. The key characteristics of any of these cages must be higher than the current predicate. 4

5 Axial Compression Test ASTM 2077 ASTM F 2077 (simplified) The cage is placed between two steel plates. The lower plate is fixed while the upper plate compresses the cage. Data acquisition system records load vs. compression data. The curves are used to identify the key characteristic of the cage such as the Yield Load The cage with the lowest Yield Load is identified as the worst case. Normalized Force Stiffness Calculated Yield Load 0.02*Gauge Length Compression Distance 5

6 Identifying the Worst Case As mentioned, more than 400 of cage designs are generated. Mechanically testing every single cage is not feasible. A rationale is needed to identify which cage/s has/have the lowest yield load under axial compression. Computational modeling is proposed to be used to create the rationale for identifying the worst case device. 6

7 <100 BT $$ >2400 BT Tests $$$$ 7

8 8 Risk Assessment Controlling Major X $$ Moderate Minor X $$$$ More than 2400 Physical Tests A B C D E F G PATIENT CONSEQUENCE DECISION INFLUENCE Necessitates medical or surgical intervention permanent impairment of body function or permanent damage to a body structure

9 Context of Use and Credibility Requirements COU is yield load in axial compression The computational model should be able to rank the cages based on their yield load in axial compression. None, All cages to be tested. Not feasible. MM, CM need to rank the cages based on the yield load in a proper order. Additional Bench top testing will be performed to evaluate the identified cages. MH, CM need to rank the cages based on the yield load in correct order and quantifies them so that they can be compared to the predicate. 9

10 Decision Influence Medium 1. Develop and validate a Computational Model (COU being yield load in axial compression). 2. Construct a DOE using the validated CM and identify the significant design factors that affect the yield load. 3. Create a Regression Model Based on the DOE Results. 4. Identify potential Worst Case sizes using the Regression Model. 5. Perform Bench top Testing of the Identified designs. 6. Perform Computational Analysis of the Identified sizes. 10

11 The Domain The cage is meshed and placed between two rigid plates. One plate is fixed in all directions while the other plate can only move in the compression direction. Moving Plate Fixed Plate 11

12 Force/Compression 12

13 Software Mesh Generation Software Hypermesh 12 (Altair) Explicit Analysis Software LS DYNA 970 SMP (Livermore Software Tech Corp) Statistical Software Minitab 16 (Minitab, Inc.) 13

14 Verification Credibility Factors Credibility Factors Validation Model Comparator Output Assessment Applicability Code Verification Solution Verification System Configuration System Properties Boundary Conditions Governing Equations Sample Characterization Control over test conditions Measurement uncertainty Discrepancy Comparison Applicability to the COU 14

15 Code Verification and Solution Verification Verification Code Verification Solution Verification Software Quality Software Architecture and Logic Numerical Verification Discretization Error Use Errors Numerical Solver Error Software Architecture and Logic, minimal Numerical Verification, minimal Discretization Error Refinement was performed with quantification of discretization error or uncertainty estimate for the variable of interest to account for discretization error; performed at the location of interest for the variable of interest Use Errors Some key inputs and outputs independently verified through peer review. Numerical Solver Error Problem specific sensitivity study performed on solver convergence tolerances and error reported. 15

16 Model Validation Is the Model sufficiently accurate for the proposed COU? System Configuration: Nominal Dimensions Native software System Properties: Generated through calibration Boundary Conditions: Variation is not expected to play a major role System Configuration System Properties Model Boundary Conditions Governing Equations 16

17 System Properties Material Characteristics Carbon Fiber Reinforced PEEK (polyetheretherketone), aka CFRP Parts are made using injection molding and machined to within spec. Brittle in tension and ductile in compression. 17

18 Material Model In this application the majority of the cage is in compression (~9 to 1 volume ratio). Bilinear kinematic plasticity model in LS DYNA, MAT3 is utilized with Four parameters, Modulus of Elasticity, E Poison s Ratio, v Yield Strength, Ys Secant Modulus, Es Stress Ys Es E Strain 18

19 Material Calibration A specific cage is used as calibration specimen. Simplified ASTM cage compression protocol is replicated. Bi linear plasticity model is used. The results are compared with those of the existing physical test. Stiffness Yield Point 19 Normalized Force Force (N) Stiffness Yield Load Sample FEA Normalized Disp Compression (mm) Distance Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 LWR

20 Material Calibration Using the calibrated material model, the yield load of 15 other cages are determined and are compared against the existing bench top test results. Size Shape Oblique Lordotic %Diff (CM vs. (WxHxL) Angle Angle Benchtop) 9X9X21 Parallel X8X21 Inline X9X21 Inline X8X25 Inline X11X21 Parallel x13x23 Oblique x9x31 Oblique x13x31 Oblique x13x23 Oblique x8x23 Parallel x9x21 Parallel x8x27 Parallel x15x27 Parallel x13x21 Inline x8x23 Oblique X8X27 Oblique

21 FEA to Test The FEA results were compared to test results for the same part numbers and there was a statistically significant relationship as the P<0.001 and P<0.05 was the criteria. There was a 0.96 correlation, with R 2 = 91.69% 21

22 Comparator Validation Sample Characterization Control over test conditions Measurement uncertainty Sample Characterization: Critical characteristics of individual articles are known, but statistical distribution is unknown. Control Over Test Conditions: Nominal settings for critical test conditions are applied and periodically verified to fall within an allowable range. Measurement Uncertainty: Measurements are obtained from instruments with known accuracy, and are monitored at critical locations. 22

23 Test Results Repeatability Tests generally repeatable Sample 1 Sample 2 Sample 3 Std Dev / Mean Yield Load = 2% (on Average) Force (N) Disp (mm) 23

24 Test Repeatability Multi-Vari Chart for measure by sample - part number measured yield (N) sample part number The Repeatability of the test method is +/ 5.3% of the measured yield. 24

25 Bench Top Test Variations/Errors/Gauge R&R Source of Variation Machine +Sensors Fixturing and Operator Variation Test sample: source material + mold process Test sample: geometry (machining tolerance) Notes Low: Calibration including repeated checks to applicable ASTM, ANSI standards Historical variation: as low as 0.5% as high as 6% Material: not suspected to be significant Mold: validated process through IQ/OQ/PQ including mechanical testing Significant effect. Drawing tolerances allow substantial range in geometric factors 25

26 Assessment of Outputs Validation Assessment of Outputs Level of equivalency of input and Rigor of output comparison output types Level of equivalency of input and output types: Equivalent Inputs Equivalent Outputs Rigor of output comparison: Quantity Multiple Outputs Rigor of Comparison: Quantitative comparison with uncertainty captured from comparator 26

27 Applicability Relevance to COU Minimal differences exist between validation space and COU Applicability Applicability to the COU Validation Points COU 27

28 DOE and the Regression Model DOE to determine factors affecting yield and to determine the size of these main effects and interactions. Multiple geometric factors were examined A regression function for the yield load is then generated based on the results of the DOE. Yield Load= A + B*X + C*Y Regression Model to Computational Model R 2 =0.96 Coded Units Non Coded Units W H L W H L

29 Full Regression Model An expanded regression model was made using the factors identified from the DOE. This model was used on all sizes and angles. 16,500 16,000 15,500 15,000 14,500 DOE Model (N) Tested Yield (N) 14,000 13,500 13,000 12,500 FEA Yield (N) baseline 12,000 11,500 11,000 10,500 10,000 9,500 9,000 8,500 8,000 7,500 7,000 6,500 6,000 5,500 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Yield (N) 29 Part

30 10,000 9,900 9,800 9,700 9,600 9,500 9,400 9,300 9,200 9,100 9,000 8,900 8,800 8,700 8,600 8,500 8,400 8,300 8,200 8,100 8,000 7,900 7,800 7,700 7,600 7,500 7,400 7,300 7,200 7,100 7,000 6,900 6,800 6,700 6,600 6,500 Regression Model/FEA/BT Test Model (N) FEA Yield (N) Model at LMC (N) Tested Yield (N) baseline Model at MMC Yield (N) Part

31 Summary Risks and the attainable certainty estimates set the decision influence to Medium. A combination of validated computational modeling and statistical modeling was used to identify potential worst performing cage. Bench top test performed on the identified worst case cages agreed with the predicted ranking. High decision influence is achievable though additional V&V activities. 31