Supervised Machine Learning and Physics based Machine Learning approach
for prediction of peak temperature distribution in Additive Friction Stir
Deposition of Aluminium Alloy
- URL: http://arxiv.org/abs/2309.06838v2
- Date: Fri, 1 Dec 2023 23:25:00 GMT
- Title: Supervised Machine Learning and Physics based Machine Learning approach
for prediction of peak temperature distribution in Additive Friction Stir
Deposition of Aluminium Alloy
- Authors: Akshansh Mishra
- Abstract summary: correlations between process parameters, thermal profiles, and resulting in AFSD remain poorly understood.
This work employs a framework combining supervised machine learning ( neural) and physics-informed networks (PINNs) to predict peak temperature distribution in AFSD from process parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Additive friction stir deposition (AFSD) is a novel solid-state additive
manufacturing technique that circumvents issues of porosity, cracking, and
properties anisotropy that plague traditional powder bed fusion and directed
energy deposition approaches. However, correlations between process parameters,
thermal profiles, and resulting microstructure in AFSD remain poorly
understood. This hinders process optimization for properties. This work employs
a framework combining supervised machine learning (SML) and physics-informed
neural networks (PINNs) to predict peak temperature distribution in AFSD from
process parameters. Eight regression algorithms were implemented for SML
modeling, while four PINNs leveraged governing equations for transport, wave
propagation, heat transfer, and quantum mechanics. Across multiple statistical
measures, ensemble techniques like gradient boosting proved superior for SML,
with lowest MSE of 165.78. The integrated ML approach was also applied to
classify deposition quality from process factors, with logistic regression
delivering robust accuracy. By fusing data-driven learning and fundamental
physics, this dual methodology provides comprehensive insights into tailoring
microstructure through thermal management in AFSD. The work demonstrates the
power of bridging statistical and physics-based modeling for elucidating AM
process-property relationships.
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