Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model

Changxuan Zhao () and Shreyes N. Melkote ()
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Changxuan Zhao: Georgia Institute of Technology
Shreyes N. Melkote: Georgia Institute of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 23, 1845-1865

Abstract: Abstract In this work, we present a deep neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining and finishing from design and manufacturing data. By concatenating a 3D Convolutional Neural Network (3D CNN) with a simple Multilayer Perceptron (MLP), we show that the model can learn the capabilities of a manufacturing process described in terms of the part features and quality it can generate, and the materials it can process. Specifically, the proposed method takes the part feature geometry, material properties, and quality information contained in a part design as inputs and trains the deep neural network model to predict the manufacturing process label as output. We present an example implementation of the proposed method using a synthesized dataset to illustrate automatic manufacturing process selection. The performance of the proposed model is compared with the performance of interpretable data-driven classification methods such as decision trees and random forests. By comparing the performance with different combinations of input information to be included during training, it is evident that part quality information is necessary for characterizing the capabilities of finishing processes while material information further improves the model’s ability to discriminate between the different process capabilities. The superior prediction accuracy of the proposed deep neural network model demonstrates its potential for use in future data-driven Computer Aided Process Planning (CAPP) systems.

Keywords: Manufacturing process capability; Data-driven; Convolutional neural network; Process selection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02134-z

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