Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model
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| Udgivet i: | Journal of Intelligent Manufacturing vol. 35, no. 4 (Apr 2024), p. 1845 |
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Springer Nature B.V.
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| Online adgang: | Citation/Abstract Full Text - PDF |
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| 100 | 1 | |a Zhao, Changxuan |u Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, USA (GRID:grid.213917.f) (ISNI:0000 0001 2097 4943) | |
| 245 | 1 | |a Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model | |
| 260 | |b Springer Nature B.V. |c Apr 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Process planning | ||
| 653 | |a Material properties | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Machining | ||
| 653 | |a Neural networks | ||
| 653 | |a Process selection | ||
| 653 | |a Finishing | ||
| 653 | |a Manufacturing | ||
| 653 | |a Machine learning | ||
| 653 | |a Computer aided operations | ||
| 653 | |a Decision trees | ||
| 653 | |a Manufacturing industry | ||
| 653 | |a Advanced manufacturing technologies | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Melkote, Shreyes N. |u Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, Atlanta, USA (GRID:grid.213917.f) (ISNI:0000 0001 2097 4943) | |
| 773 | 0 | |t Journal of Intelligent Manufacturing |g vol. 35, no. 4 (Apr 2024), p. 1845 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2984718007/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2984718007/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |