Intelligent milling tool wear estimation based on machine learning algorithms

Karabacak, Yunus Emre

doi:10.1007/s12206-024-0131-z

J Mech Sci Technol

2024

DOI: 10.1007/s12206-024-0131-z

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Intelligent milling tool wear estimation based on machine learning algorithms

Yunus Emre Karabacak

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2024

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Cited by 3 publications

References 60 publications

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Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Ross,

Mashinini,

Mishra

et al. 2024

Journal of Materials Research and Technology

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Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Ross,

Mashinini,

Mishra

et al. 2024

Journal of Materials Research and Technology

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Predicting mechanical behavior of different thin-walled tubes using data-driven models

Kuleyi̇n,

Karabacak,

Gümrük

2024

Materials Today Communications

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A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm

Wu,

Wang,

Chen

2024

Mathematics

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Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method’s superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems.

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Intelligent milling tool wear estimation based on machine learning algorithms

Cited by 3 publications

References 60 publications

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Predicting mechanical behavior of different thin-walled tubes using data-driven models

A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm

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