Browsing by Author "Gavina, C.G."

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    A smart manufacturing framework for tool wear analysis and RUL estimation using multimodal deep learning
    (SAGE Publications Ltd, 2025) Gavina, C.G.; Hemalatha, K.L.; Ranganath, K.J.; Rajanna, S.; H, S.N.
    The machining sector faces ongoing challenges in enhancing cutting tool efficiency and accurately forecasting tool wear, essential for increasing productivity and lowering operational expenses. This research presents an innovative multimodal deep learning architecture designed to predict the Remaining Useful Life (RUL) of cutting tools, providing a solid solution for predictive maintenance in Industry 5.0. By incorporating advanced computer vision methods with real-time sensor data evaluations, the framework delivers a thorough system for assessing tool conditions. The Mod-R2AU-Net is utilized for precise segmentation of tool wear, achieving a validation accuracy of 97.65% alongside a loss of 0.1711. At the same time, CNN models evaluate the tool wear area, yielding vital insights into degradation. These features extracted from images are merged with experimental sensor data (including speed, feed, depth of cut, force, temperature, and vibration) to form a multimodal dataset. XGBoost is applied for the classification of tool wear, attaining an accuracy of 98%, whereas Multi-Layer Perceptron (MLP) models forecast wear with an R2 of 0.9790 and an RMSE of 0.0063. Furthermore, the Differential Evolution-optimized BiLSTM (DE-BiLSTM) model provides the most precise RUL predictions, achieving an R2 of 0.9992 and surpassing conventional LSTM and BiLSTM models. This pioneering multimodal technique not only enhances predictive maintenance capabilities but also facilitates the optimization of tool usage, reduces unplanned downtimes, and produces notable economic benefits, positioning the machining industry at the leading edge of Industry 5.0. © The Author(s) 2025
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    Tool health monitoring in lathe turning process by artificial intelligence techniques — a review
    (SAGE Publications Ltd, 2025) Gavina, C.G.; Hemalatha, K.L.; Ranganath, K.J.; Rajanna, S.; Shivananda Nayaka, H.S.
    Monitoring tool health is essential for maintaining efficiency, productivity, and quality in lathe turning operations. Traditional methods rely on manual assessments and subjective judgments, which can be time-consuming, inconsistent, and inadequate for detecting subtle tool wear. Therefore, this review discusses the literature review on predicting tool wear in the turning process, comprehensively examining the methods documenting for sensing and testing parameter design, image processing, and classification methods. The review outlines the use of vibration signals and images as datasets and advanced artificial intelligence techniques like machine learning, computer vision, deep learning, and expert systems to predict the accurate wear percentage in the tool. It also discusses the benefits and limitations of methods used in reviewed papers. To conclude, the performance of AI techniques from the reviewed papers, RNN from deep learning, gives more accuracy, with 97.04% predicting the tool wear. Within the Industry 4.0 framework, after a detailed review of the AI techniques, the combination of deep learning techniques that ensemble vibration signals and image information develops as a vital technology for evolving intelligent manufacturing. © The Author(s) 2024