Convolutional and generative adversarial neural networks in manufacturing |
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Authors: | Andrew Kusiak |
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Affiliation: | 1. Department of Industrial and Systems Engineering, The University of Iowa, Iowa City, IA, USAandrew-kusiak@uiowa.edu https://orcid.org/0000-0003-4393-1385 |
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Abstract: | Manufacturing is undergoing transformation driven by the developments in process technology, information technology, and data science. A future manufacturing enterprise will be highly digital. This will create opportunities for machine learning algorithms to generate predictive models across the enterprise in the spirit of the digital twin concept. Convolutional and generative adversarial neural networks have received some attention of the manufacturing research community. Representative research and applications of the two machine learning concepts in manufacturing are presented. Advantages and limitations of each neural network are discussed. The paper might be helpful in identifying research gaps, inspire machine learning research in new manufacturing domains, contribute to the development of successful neural network architectures, and getting deeper insights into the manufacturing data. |
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Keywords: | manufacturing smart manufacturing intelligent manufacturing deep learning machine learning convolutional neural networks generative adversarial networks |
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