Yazar "Chauhan, Sumika" seçeneğine göre listele

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    Configuration of tool wear and its mechanism in sustainable machining of titanium alloys with energy signals
    (Springer London Ltd, 2024) Vashishtha, Govind; Chauhan, Sumika; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Ross, Nimel Sworna; Zimroz, Radoslaw; Krolczyk, Grzegorz M.
    Surface quality, machining efficiency, and tool life are all significantly impacted by tool wear in metal cutting machining. Research priorities and areas of focus in tool wear are shifting as intelligent machining becomes the norm. Unfortunately, there are currently no acknowledged most effective ways for analyzing tool based on the energy signals specially in the machining of titanium and its alloys. In the present work, the titanium machining was performed under different lubrication conditions such as dry, minimum quantity lubrication (MQL), liquid nitrogen and hybrid, etc. Then, the spectrograms are used to transform the acquired energy data into time-frequency features. Starting with a set of randomly generated hyper parameters (HPs), the long short-term memory (LSTM) model is fine-tuned using sine cosine algorithm (SCA) with loss serving as the fitness function. The confusion matrix provides additional validation of the 98.08% classification accuracy. Additional evaluations of the suggested method's superiority include its specificity, sensitivity, F1-score, and area under the curve (AUC).
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    Parallel structure of crayfish optimization with arithmetic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery applications
    (Elsevier, 2024) Chauhan, Sumika; Vashishtha, Govind; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Demirsoz, Recep; Noman, Khandaker; Kolesnyk, Vitalii
    Intelligent techniques play a vital role in predicting the friction force during the wear of Ti-6Al-4V alloy under different lubricating conditions. The effective assessment of friction forces and lubricating conditions allows for the replacement of the material before catastrophic failure. However, it remains challenging to utilise friction forces under different lubrication conditions to predict the wear through intelligent techniques. In this work, an advanced technique based on artificial intelligence has been proposed to address this issue. Intially parallel structure of crayfish optimization and arithmetic optimization algorithm (PSCOAAOA) is developed to duly address the issues of slow convergence, stucking in local optima and quality of the solution. The PSCOAAOA is further implemented for finding the optimal parameters (regularization parameter and kernel function) of the Support Vector Machine (SVM). The quantitative and qualitative analysis of PSCOAAOA is carried out on CEC2014 benchmark functions to validate its efficacy and robustness. The friction force generated during wear testing under different lubricating conditions is bifurcated into training and test data. Out of which, training data trains the SVM at an optimal combination of parameters. The overall accuracy of the built SVM model is found to be 95.85% with a computation time of 26.85 s.
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    Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models
    (Elsevier Sci Ltd, 2023) Korkmaz, Mehmet Erdi; Gupta, Munish Kumar; Kuntoglu, Mustafa; Patange, Abhishek D.; Ross, Nimel Sworna; Yilmaz, Hakan; Chauhan, Sumika
    Machine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances.