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Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process

1
The George W, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2
Department of Computer Science, University of Georgia, Athens, GA 30605, USA
3
Department of Statistics & Data Science, University of Central Florida, Orlando, FL 32816, USA
4
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2019, 3(3), 57; https://doi.org/10.3390/jmmp3030057
Received: 11 June 2019 / Revised: 9 July 2019 / Accepted: 11 July 2019 / Published: 14 July 2019
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Abstract

Electrochemical machining is a promising non-traditional manufacturing process to make high-quality parts. The benefits of minimal thermally and mechanically induced stresses, free of burr, and a low surface roughness are appealing for industry and research institutes. However, the combined chemical reaction, electric field, fluid mechanics, and material properties involve a significant number of independent parameters which are difficult to analyze in order to draw comprehensive conclusions. To our current knowledge, process responses such as the material removal rate, optimal feed rate, and cutting profile cannot be represented accurately by analytical solutions. In recent years, deep learning has had tremendous success in analyzing sophisticated systems. The improved computation efficiency and reduced size of the training dataset required for deep learning have enabled various prediction models in the manufacturing industry. In this paper, a new approach is developed using the deep convolutional network with the Bayesian optimization algorithm to predict the diameters of the drilled hole from an electrochemical machining process. The Keras application programming interface (API) was used to build the deep convolutional network; the feed rate, pulse-on time, and voltage were used as input parameters to provide a fair comparison with a neural network from previous research. Random dropout layers were added to prevent overfitting of the network. Instead of tuning the network parameter by trial and error, the Bayesian parameter optimization algorithm was implemented to find the optimal set of parameters of the deep convolutional network that yields the minimum mean square error. The proposed algorithm was compared with a previously developed neural network with partially embedded physical knowledge. Improved training speed and accuracy were observed in comparison with the traditional neural network. The prediction model using the proposed deep learning algorithm demonstrated better prediction accuracy and provided a more systematic way to select the hyperparameter for the deep convolutional network. View Full-Text
Keywords: Bayesian optimization; convolutional neural network; deep learning; electrochemical micro-machining Bayesian optimization; convolutional neural network; deep learning; electrochemical micro-machining
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Lu, Y.; Wang, Z.; Xie, R.; Liang, S. Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process. J. Manuf. Mater. Process. 2019, 3, 57.

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