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Artificial Neural Networks: A Manufacturing Engineering Perspective and Case Study Review

Eric Dimla
School of Science and Technology, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam
Abstract—This paper presents a brief review of Artificial Neural Network (ANN) application in a typical manufacturing engineering scenario. The discussion in the first part centres on the underlying principles and learning algorithms with emphasis on the basic structure of ANNs. It would be extremely laborious and tedious to list all types of neural networks herein but for the purpose of this study, an overview of those networks with proven manufacturing engineering applications was deemed necessary. The merits of ANNs and their applicability was demonstrated by reviewing work performed within the last decade in the chosen area of manufacturing engineering application, specifically Tool Condition Monitoring (TCM) in metal cutting operations.

Index Terms—ANNs, learning in ANNs, Tool wear monitoring

Cite: Eric Dimla, "Artificial Neural Networks: A Manufacturing Engineering Perspective and Case Study Review," Journal of Communications, vol. 14, no. 8, pp. 636-646, 2019. Doi: 10.12720/jcm.14.8.636-646
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