Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11532
Title: Artificial Intelligence for Machining Process Monitoring
Authors: Ünver, H.
Özbayoğlu, A.M.
Söyleyici, C.
Çelik, B.B.
Keywords: Artificial intelligence
Chatter
Deep learning
Feature engineering
Machine learning
Machining
Process monitoring
Tool wear
Transfer learning
Publisher: Elsevier
Abstract: Since the early 1980s, implementation of artificial intelligence (AI)-based intelligent machining process monitoring (MPM) has been advancing, parallel to the new AI models and machining technologies. These systems are critical for balancing the tradeoffs among productivity, quality, cost, and sustainability measures of machine tool shops and the broader manufacturing industry. Furthermore, increased demand for high-level process automation, pressure to use less workforce, and data explosion by widely used low-cost sensory equipment have increased the expectation from MPM to become an enabler of the dark factory. This chapter covers a broad range of AI models and techniques used in MPM by providing both algorithmic fundamentals and architectural examples from recent studies. © 2024 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/B978-0-323-99134-6.00010-4
https://hdl.handle.net/20.500.11851/11532
ISBN: 9780323991346
9780323996723
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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