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<p>In precision manufacturing, the selection of cutting tools and uncontrolled tool wear is a major challenge which directly affects manufactured product quality, surface integrity and process efficiency. This work uses advanced deep learning (DL) architectures: InceptionNetv3, ResNeXt, and InceptionResNetv2 that have been tuned through metaheuristic optimization strategies to monitor and choose milling cutters. The Tunable Q Wavelet Transform (TQWT) is used in the proposed approach to preprocess and convert vibration signals to scalograms, which act as an input feature for deep learning models. To further improve prediction accuracy, each model’s hyperparameters are adjusted using Ant Lion Optimization (ALO) and the Clonal Selection Algorithm (CSA). Experimental investigations on milling dataset demonstrate that, the CSA-optimized InceptionResNetv2 exhibited the average prediction accuracy (98.25%), according to five-fold cross-validation for cutter c4. The ALO and CSA tuned models demonstrate improved generalization through five-fold cross validations ensuring robustness under varying operating and cutter conditions. The proposed approach offers a computationally efficient and scalable solution for real time cutter selection and tool wear monitoring in industrial environments. By eliminating conventional feature extraction and enabling adaptive learning from vibration based scalograms, the proposed framework can be integrated in Industry 4.0 manufacturing systems for predictive maintenance and process optimization.</p>

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An integrated TQWT-deep learning framework for intelligent tool cutter selection using clonal selection and ant lion optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Vinay Vakharia,
  • Saurabh Tiwari,
  • Hitesh Panchal,
  • Choon Kit Chan,
  • Saurav Dixit,
  • Mohd Aamir Mumtaz,
  • Muhammad Imran Khan

摘要

In precision manufacturing, the selection of cutting tools and uncontrolled tool wear is a major challenge which directly affects manufactured product quality, surface integrity and process efficiency. This work uses advanced deep learning (DL) architectures: InceptionNetv3, ResNeXt, and InceptionResNetv2 that have been tuned through metaheuristic optimization strategies to monitor and choose milling cutters. The Tunable Q Wavelet Transform (TQWT) is used in the proposed approach to preprocess and convert vibration signals to scalograms, which act as an input feature for deep learning models. To further improve prediction accuracy, each model’s hyperparameters are adjusted using Ant Lion Optimization (ALO) and the Clonal Selection Algorithm (CSA). Experimental investigations on milling dataset demonstrate that, the CSA-optimized InceptionResNetv2 exhibited the average prediction accuracy (98.25%), according to five-fold cross-validation for cutter c4. The ALO and CSA tuned models demonstrate improved generalization through five-fold cross validations ensuring robustness under varying operating and cutter conditions. The proposed approach offers a computationally efficient and scalable solution for real time cutter selection and tool wear monitoring in industrial environments. By eliminating conventional feature extraction and enabling adaptive learning from vibration based scalograms, the proposed framework can be integrated in Industry 4.0 manufacturing systems for predictive maintenance and process optimization.