AI-based inspection of the axes of machine tools

Demetgul, Mustafa; Wang, Wei; Fleischer, Jürgen; Tansel, Ibrahim Nur

doi:10.1007/s00170-023-12830-y

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2024

2025

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Cited by 2 publications

References 33 publications

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Review of machine learning applications in additive manufacturing

Inayathullah,

Buddala

2025

Results in Engineering

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Review of machine learning applications in additive manufacturing

Inayathullah,

Buddala

2025

Results in Engineering

View full text Add to dashboard Cite

Sensorless Tool Wear Estimation by using the Artificial Intelligence (AI) tools from the currents of motors generating linear motions

Demetgul,

Darji,

Tansel

et al. 2024

Preprint

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Timely replacement of cutting tools reduces machining costs and prevents the manufacture of defective products. Many researchers have developed Tool Condition Monitoring (TCM) systems to estimate tool wear using reliable, low-cost instrumentation. This paper proposes estimating tool wear by interpreting motor current signals from the programming logic controllers (PLCs) of CNC machines with artificial intelligence (AI) tools. Experimental data were collected from three cutting tools at four wear states: normal, and with one, two, or three worn cutting edges. Four AI models were used to classify normal and anomalous cases: 1D Convolutional Neural Networks (1D CNN), Long Short-Term Memory (LSTM) networks, a hybrid 1D CNN-LSTM architecture, and an LSTM autoencoder. Hyperparameter tuning was performed to optimize each model. Three approaches were explored to address different production needs: · Approach A - Supervised Learning: 1D CNN showed the best performance, achieving 90% validation accuracy. · Approach B - Semi-Supervised Learning: An LSTM autoencoder trained only on “normal” data achieved 96% validation accuracy but a low F1 score of 0.51, indicating limited anomaly detection capability. · Approach C - Transfer Learning: Using transfer learning, the 1D CNN model reached 85% validation accuracy and an F1 score of 0.84 when tested on a third tool with a different diameter.

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AI-based inspection of the axes of machine tools

Cited by 2 publications

References 33 publications

Review of machine learning applications in additive manufacturing

Review of machine learning applications in additive manufacturing

Sensorless Tool Wear Estimation by using the Artificial Intelligence (AI) tools from the currents of motors generating linear motions

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