Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/34
Title: Application of Artificial Neural Network for Modeling Surface Roughness in Machining Process—A Review
Authors: Pahuja, Vipin
Kant, Suman
Jawalkar, C. S.
Verma, Rajeev
Keywords: Artificial neural network
Back-propagation
Modeling
Machining
Performance
Surface roughness
Issue Date: Jul-2021
Publisher: Springer International Publishing
Series/Report no.: Lecture Notes on Multidisciplinary Industrial Engineering;July 2021
Abstract: In manufacturing industries, machining is the most important and widely used process. Modeling and optimization are two main objects in the machining process. For modeling any machining process, it requires the basic mathematical models for the formulation of process objective functions. The artificial intelligence methods such as artificial neural network have been applied by many authors due to more complex and nonlinear behavior of the machining process. This paper presents a comprehensive review of development and uses of artificial neural network in modeling surface roughness in machining. According to the previous study done by various authors, the capabilities and drawbacks of the ANN methods in modeling surface roughness have been presented. In addition, the future behavior of ANN in the modeling machining process has also been presented.
Description: Application of Artificial Neural Network for Modeling Surface Roughness in Machining Process—A Review
URI: http://localhost:8080/xmlui/handle/123456789/34
ISBN: 978-981-15-4549-8
978-3-030-73494-7
ISSN: 2522-5022
Appears in Collections:Faculty of Manufacturing Skills Education

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