October AMP_Digital

A D V A N C E D M A T E R I A L S & P R O C E S S E S | O C T O B E R 2 0 1 9 2 1 CONCLUSIONS AND FUTURE WORK The overall methodology de- scribed here effectively improves the costly trial-and-error processes conven- tionally used for parameter develop- ment and alloy discovery. Future work will improve the methodology through the coupling of physics-informed mod- els with the experimental and data-driv- en approaches previously mentioned. Incorporating probabilistic ML, intelli- gent sampling, and optimization pro- tocols, coupled with high-throughput printing and characterization into these processes is a substantial leap toward the ultimate goal of enabling rapid in- sertion, robust optimization, and thor- ough qualification of new additive materials for use in advanced applica- tions. ~AM&P For more information: Joe Vinciquerra is senior principal engineer and addi- tive platform leader, GE Research, One Research Circle, Niskayuna, NY 12309, 518.387.6626 , joseph.vinciquerra@ ge.com. References 1. L.N. Carter, M.M. Attallah, and R.C. Reed, Laser Powder Bed Fabrication of Nickel-Base Superalloys: Influence of Parameters; Characterization, Quan- tification and Mitigation of Cracking, 12th Intl. Symp. on Superalloys, TMS, p 577-586, 2012. 2. M. Tang, P.C. Pistorius, and J.L. Beuth, Prediction of Lack-of-Fusion Por- osity for Powder Bed Fusion, Additive Manufacturing , 14, p 39-48, 2017. 3. L. Johnson, et al., Assessing Print- ability Maps in Additive Manufacturing of Metal Alloys, Acta Materialia, 176, p 199-210, Sept 2019. 4. M. Luo and Y.C. Shin, Estimation of Keyhole Geometry and Prediction of Welding Defects during Laser Welding Based on a Vision System and a Radial Basis Function Neural Network, Intl. J. Adv. Manuf. Technol., 81(1-4) p 263-276, 2015. 5. L. Scime and J.L. Beuth, Using Machine Learning to Identify In-Situ Melt Pool Signatures Indicative of Flaw Formation in a Laser Powder Bed Fusion Additive Manufacturing Process, Additive Manufacturing, 25, p 151-165, 2019. 6. A. Forrester, A. Sobester, and A. Keane, Engineering Design via Surrogate Modelling: a Practical Guide, John Wiley & Sons, 2008. 7. C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995. 8. C.E. Rasmussen, Gaussian Pro- cesses in Machine Learning, Summer School on Machine Learning, p 63-71. Springer, Berlin, Heidelberg, Feb 2003. 9. R.G. Ghanem and P.D. Spanos, “Stochastic Finite Elements: a Spectral Approach,” Courier Corp., 2003. 10. M.C. Kennedy and A. O’Hagan, Bayesian Calibration of Computer Models, J. Royal Statistical Soc.: Series B (Statistical Methodology), 63(3), p 425-464, 2001. 11. S. Chib and E. Greenberg, Un- derstanding the Metropolis-Hastings Algorithm, The American Statistician, 49(4), p 327-335, 1995. 12. A.K. Subramaniyan, N.K. Chen- nimalai, and L. Wang, Probabilistic Validation of Complex Engineering Simulations with Sparse Data, ASME Turbo Expo 2014: Turbine Tech. Conf. & Expo., ASME Digital Collection, June 2014. 13. N.C. Kumar, A.K. Subramaniyan, and L. Wang, Improving High-Dimensional Physics Models through Bayesian Cali- bration with Uncertain Data, ASME Turbo Expo 2012: Turbine Tech. Conf. & Expo., ASME Digital Collection, p 407-416, June 2012. 14. J. Kristensen, et al., Industrial Applications of Intelligent Adaptive Sampling Methods for Multi-Objective Optimization, IntechOpen, 2019. 15. K.S. Aggour, et al., Artificial Intel- ligence/Machine Learning in Manufac- turing and Inspection: A GE Perspective, MRS Bulletin, 44(7), p 545-558, 2019. 16. K.S. Aggour, et al., Application of Data Models to Artificial Intelligence to Improve Fleet Readiness, ASNE Intelligent Ships Symposium (ISS), April 2019. 17. J.W. Williams, et al., Semantics for Big Data Access & Integration: Improving Industrial Equipment Design through Data Usability, Proc. IEEE Intl. Conf. on Big Data, p 1103-1112, 2015. 18. L.N. Carter, et al., The Influence of the Laser Scan Strategy on Grain Structure and Cracking Behaviour in SLM Powder-Bed Fabricated Nickel Superalloy, J. of Alloys and Compounds, 615, p 338-347, Dec 2014. 19. V.D. Divya, et al., (2016, April), Microstructure of Selective Laser Melted CM247LC Nickel-Based Superalloy and its Evolution through Heat Treatment, Matls. Character., 114, p 62-74, April 2016. 20. S. Catchpole-Smith, et al., Fractal Scan Strategies for Selective Laser Melting of ‘Unweldable’ Nickel Super- alloys, Additive Manufacturing, 15, p 113-122, May 2017.