Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak, Yunus Emre

doi:10.17531/ein/168082

Artykuł - szczegóły

Tytuł artykułu

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Autorzy

Karabacak Yunus Emre

Treść / Zawartość

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DOI

10.17531/ein/168082

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Języki publikacji

Abstrakty

CNC milling machines are frequently used in the manufacturing of mechanical parts in the industry. One of the most important components of milling machines is the cutting tool. Monitoring the cutting tool wear is important for the reliability, continuity, and quality of production. Monitoring the tool and detecting the stage of wear are difficult processes. In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

Słowa kluczowe

wear stage estimation milling convolutional neural network time-frequency analysis

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2023

Tom

Vol. 25, no. 3

Strony

art. no. 168082

Opis fizyczny

Bibliogr. 65 poz., rys., tab., wykr.

Twórcy

autor

Karabacak Yunus Emre

karabacak@ktu.edu.tr

Department of Mechanical Engineering, Karadeniz Technical University, Turkey

https://orcid.org/0000-0002-0268-3656

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

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Bibliografia

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bwmeta1.element.baztech-629e1c3f-da25-400c-ae9b-072b82638ee3