Nonlinear Optimization Algorithms for Adjusting Selective Laser Melting Conditions

—The possibility and validity of using the mathematical algorithms of nonlinear optimization and machine learning to describe the dependence of the “input” parameters of a setup on the “output” parameters of the synthesized material during selective laser melting of a metal powder of a nickel-based...

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Bibliographic Details
Published in:Russian metallurgy Metally 2022-12, Vol.2022 (13), p.1680-1691
Main Authors: Aslanyan, G. G., Sukhov, D. I., Min, P. G., Peskova, A. V.
Format: Article
Language:English
Subjects:
Algorithms
Chemistry and Materials Science
Laser beam melting
Laser beam welding
Machine learning
Materials Science
Metal powders
Metallic Materials
Methods of Studying the Structure and Properties of Materials. Process Modeling
Nickel base alloys
Optimization
Parameters
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Summary:—The possibility and validity of using the mathematical algorithms of nonlinear optimization and machine learning to describe the dependence of the “input” parameters of a setup on the “output” parameters of the synthesized material during selective laser melting of a metal powder of a nickel-based limitedly weldable alloy are considered. The problem is formulated, and the validity of its mathematical solution is substantiated. A mathematical model is developed, and its predictive abilities and the accuracy of predicting the synthesis condition parameters for an “ideal” sample are estimated.
ISSN:0036-0295
1555-6255
1531-8648
DOI:10.1134/S0036029522130018