Multiple input CNN architecture for tool state recognition in the milling process based on time series signals

• Autorzy: Bukowski Michał , Antoniuk Izabella , Szymanowski Karol , Krupa Artur , Kurek Jarosław

Identyfikatory

DOI

10.37190/ord240303

• Języki publikacji: EN

Abstrakty

EN

The study presents a tailored application of a multiple-input convolutional neural network (CNN) for tool state recognition in the milling process. Our approach uniquely applies an 11-input CNN to classify tool wear in chipboard milling, utilizing scalogram images derived from time-series signals. The primary objective was to categorize tool wear into three classes: green, yellow, and red, signifying the progression of wear. The study involved 75 samples (25 samples per class), each comprising 11 signals transformed into scalograms via continuous wavelet transform. The dataset of 825 scalogram images enabled the development of a CNN-based diagnostic model, achieving a notable accuracy of 96.00%, which is an improvement over a previous methods (93.33%).

Słowa kluczowe

EN

convolution neural network; tool condition monitoring; multiple input convolution neural network; deep learning

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2024
• Tom: Vol. 34, No. 3
• Strony: 41--60
• Opis fizyczny: Bibliogr. 31 poz., rys.

Twórcy

autor

Bukowski Michał

• Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

• https://orcid.org/0000-0003-1567-879X

autor

Antoniuk Izabella

• Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

• https://orcid.org/0000-0003-3157-3381

autor

Szymanowski Karol

• Department of Mechanical Processing of Wood, Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, Warsaw, Poland

• https://orcid.org/0000-0003-0383-0897

autor

Krupa Artur

• Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

• https://orcid.org/0000-0001-8583-9873

autor

Kurek Jarosław

• Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

• https://orcid.org/0000-0002-2789-4732

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).