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Physics-Embedded Machine Learning: Case Study with Electrochemical Micro-Machining

1
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2
Mechanical Engineering College, Donghua University, Songjiang District, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaoliang Jin and Dan Zhang
Machines 2017, 5(1), 4; https://doi.org/10.3390/machines5010004
Received: 29 October 2016 / Revised: 11 December 2016 / Accepted: 11 January 2017 / Published: 17 January 2017
(This article belongs to the Special Issue Precision Manufacturing Processes)
Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics of the process. In this paper, a new approach is presented for taking advantage of the partial knowledge available about the mechanisms of the process and embedding it into the neural network structure. To validate the proposed approach, it is used to create a forward prediction model for the process of electrochemical micro-machining (μ-ECM). The prediction accuracy of the proposed approach is compared to the prediction accuracy of pure neural structure models with different structures and the results show that the Neural Network (NN) models with embedded knowledge have better prediction accuracy over pure NN models. View Full-Text
Keywords: electrochemical micro-machining; neural network; embedded partial knowledge electrochemical micro-machining; neural network; embedded partial knowledge
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Lu, Y.; Rajora, M.; Zou, P.; Liang, S.Y. Physics-Embedded Machine Learning: Case Study with Electrochemical Micro-Machining. Machines 2017, 5, 4.

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