1. 6/11/20161 Process Optimisation For Micro Laser Welding in Fibre Optics Asif Malik Supervisors: Prof. Chris Bailey & Dr. Stoyan Stoyanov 14 May 2008

2. Previous Discussions Part 1: Stake Welds (Weld Spot Formation and Birefringence) Key Process Variables –Sleeve Gap (1 – 20 micons) –Laser Intensity (20% variation) – Includes reflectivity as well. –Targeting (Angle of Attack, Angle of Distribution) – up to 200 microns movement of centre point. In or y-direction. Key Questions –What is the stress profile in the fibre for the above? –How sensitive are changes to stress contours to above process variables? –What is the effect of non bridged welds with wide sleeve gaps (i.e. two welds bridged and one hasn’t)?

3. Previous Discussions Part 2: Edge Welds (Weld Spot Formation + Fibre Alignment) Key Process Variables –Sleeve to Lens Gap (Data to be provide by Bookham – 1 to 20 micros?) –Targeting (Angle of Attack, Angle of Distribution) – up to 200 microns movement of centre point (x or y-axis). Also along the z-axis. –Laser Intensity (20% Variation) Key Questions –What is the lateral Fibre displacement (how far sideways is the fibre moved based on above variables) –How sensitive is above to above process variables.

4. Project Overview Two Years completed Focus on: –Spot Welds –Design for Manufacture Methodology Report after the first year submitted Progress in the last year (this presentation) 6/11/20164

5. 5 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

6. 6/11/20166 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

7. 6/11/20167 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

8. The Model 6/11/20168 8/9 Two dimensional model and three dimensional model showing the domain of the weld spots for the fibre pig tail.

9. Numerical Model 6/11/20169

10. 10 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

11. 6/11/201611 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

12. DoE Methods Nominal Design Screening Analysis Design Full Factorial Design Central Composite Face- centered Design Latin Hypercube Design Fractional Factorial Design Plackett-Burman Design Orthogonal Array Design Three-level Factorial Design Box-Behnken Design Notz Design Koshal Design Random Design D-optimal Design Design of Experiments (DoE) is specific arrangement of points in the design space where the input parameters are varied in a carefully structured pattern so as to maximize the information that can be extracted from the resulting simulations X1 X3 X2 Centre Point Axial Point Factorial Point Central Composite DoE in 3D

13. 6/11/201613 Micro Laser Welding Induced Stress Consider Two Design Variables –Inter weld distance –Gap between the weld sleeve and ferrule Milling Depth Input Process ParametersDescriptionDesign Limits DistanceDistance between the weld spots0 - 5mm GapGap between the ferrule and weld sleeve0-40µm PowerPower of the laser AngleAngle of incidence of the laser Spot sizeLaser spot size Sleeve sizeThickness of the sleeve Inter weld distance 2mm

14. 6/11/201614 Design of Experiments Stress Predictions –Inter weld distance –Gap between weld sleeve & ferrule DOE data Milling Depth Input process parameters Design Limits Scaled Design Limits Gap 1, 20, 40 (µm) -1 to 1 Distance0, 2, 5 (mm)-1 to 1 DOE Point XDistanceGap Stress Mpa 0 141 1-0.023140 21141 3-0.2118 4-0.2-0.023115 5-0.21113 6187.2 71-0.02385.9 81184.9

15. 6/11/201615 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

16. 6/11/201616 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

17. DoE Response Surface DV2 DV1 Design Space Responses from FEM Analysis Experimental points Objective function Design Variables Minimize objective DV1 DV2 Example of composite DoE in 2 design variable space

18. 6/11/201618 Response Surface Approximation Model Stress= Function of Distance and Gap Constant a0 109.3 distance a1 -27.3 gap a2 -1.25 distance * gap a3 -0.44 distance 2 a4 3.611 gap 2 a5 0.575 Full quadratic polynomial model Stress in Fibre (distance, gap) = a0 + a1 * distance + a2 * gap + a3 * gap * distance + a4 * distance 2 + a5 * gap 2 Coefficient of Variation : 0.01210116 Adjusted R**2 : 99.66%

19. 6/11/201619 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

20. 6/11/201620 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

21. 6/11/201621 Sensitivity Analysis Process Variable Influence on Stress in Fibre Distance is more important than the gap.

22. 6/11/201622 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

23. 6/11/201623 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

24. 6/11/201624 Risk or Six-Sigma Analysis Define Uncertainty of Data Inputs Use Model of Process Run Monte Carlo Simulation Predict Process Capability

25. 6/11/201625 Process and Product Capabilities Standard Metrics (Cp; Cpk) Example (Cp) σ = Standard Deviation Process Capability –1 < Cp < 2 (Capable) –Cp < 1 (Not Capable) Upper Specification Limit (USL) Lower Specification Limit (LSL) Specified Range Process Range or

26. Define Input Data Uncertainty Distribution 6/11/201626 Distribution for inter weld distance Distribution for the gap We know – The weld spot moves from the target upto 200µm in either the x or y direction The gap has a range of values between 0 and 40µm Input Range 0 - 5 mm For Distance 0 - 40 µm For Gap Variations in input Mean & Standard Deviation (σ) Distribution Type Distance (mm) 5 & 70x10 -6 Normal Gap (µm) Range 0 - 40 µmUniform

27. Risk Analysis Case 1: Stress Distribution 6/11/201627 Customer specifications –LSL = 81.00MPa –USL = 90.00MPa 99.996% within above limits After Monte Carlo Simulation Mean85.81 Cp1.32

28. 6/11/201628 Risk Analysis Case 2: Stress Distribution Customer specifications –LSL = 81.00MPa –USL = 88.00MPa 93.8% within above limits Certainty % Gallium After Monte Carlo Simulation Mean85.80 Cp0.922

29. 6/11/201629 Presentation Outline Design for Manufacture Methodology Model of the welding process Design of experiments Reduced order modeling Sensitivity analysis Uncertainty and Risk Analysis Process and Product Capabilities (6 sigma) Conclusion and future work

30. 6/11/201630 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

31. 6/11/201631 Summary for Risk Analysis and Process Capability for Welding Process DistanceMin = 0mm Max = 5mm GapMin = 0um Max = 40um Process Capability Case 1 Customers Specification81 (LSL) to 90 (USL) Mean85.81 Cp / Cpk1.32 / 1.30 Certainty100.00% Case 2 Customers Specification81 (LSL) to 88 (USL) Mean 85.80 Cp / Cpk0.922 / 0.557 Certainty93.80%

32. 6/11/201632 Integrated Numerical Analysis Framework Forecast Uncertainty, Process/Product Capability Reduced Order Model Generation Design of Experiment High Fidelity Model Risk Analysis Optimisation Sensitivity Analysis Key process/product parameters Process/Product parameters Decision: alternativesDecision: Optimal Design Reduced Order Modelling + Sensitivity Analysis Uncertainty Analysis Design Data Uncertainty

33. Optimisation Process OPTIMISER Deterministic Optimisation, Uncertainty Quantification, Sensitivity Analysis Reliability Based Optimisation COMPUTATIONAL MODEL High Fidelity Models, Reduced Order Models INPUTS OUTPUTS

34. Optimisation study 6/11/201634 Optimisation task Minimise Stress in Fiber Subject to: 0 ≤ Distance ≤ 5 mm 0 ≤ Gap ≤ 40 um Numerical Optimisation techniques used to solve the task Optimal design Distance = 5 mm Gap = 40 um Optimal stress = 84 MPa

35. 6/11/201635 Conclusions Design for Manufacture Methodology Formulated –Based on Laser Welding Process –Fast Model for Process Evaluation –Identifies Process Performance Process Capabilities Significance of Process Parameters Design for Six-Sigma Manufacturing

36. Plan for Future Work Current Focus is on Spot Welds Edge Welds? Further refine model of the welding process Additional process / design parameters? Validation – Can we obtain data from assembly plants? 36

37. Actions - Bookham Measure Polorisation Extinction (PE) and stresses. –PE will be based on Process Variables: Distance between spots; Gap; Power in each weld spot Is stress a linear function of power density; spot size. If power varies by 20% does stress vary by 20%? Provide critical stress at which PE becomes significant. –Does Welding Process result in significant PE losses? Provide Greenwich with Materials and Thermal Data –Adhesive; Zirconia Oxide –Does adhesive stress relieve after welding process which affects PE Stake Welds – Current Process: –1mm between welds. Bookham to look at PE data after (a) Spot Weld and then (b) Edge Weld Process. Where does PE losses happen?

38. Greenwich Actions Greenwich to consider outputs based on Polorisation Extinction (PE) –Stress – Birefringence – PE Include epoxy in the model. Use data provided by Bookham. Undertake full thermo-mechanical transient analysis. Investigate: –Gap; Spot distance; Power –Relate to PE. What variation in process parameters leads to significant changes in PE?