A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys
Abstract Conventional machining often results in excessive damage to machined equipment and the surfaces generated during the process. To address these limitations, the manufacturing industry has increasingly shifted towards non-traditional machining methods. These techniques enable contactless mach...
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Springer
2025-07-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07385-4 |
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| author | Lokesh Kumar Ashish Goyal Vimal Kumar Pathak Abhijit Bhowmik |
| author_facet | Lokesh Kumar Ashish Goyal Vimal Kumar Pathak Abhijit Bhowmik |
| author_sort | Lokesh Kumar |
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| description | Abstract Conventional machining often results in excessive damage to machined equipment and the surfaces generated during the process. To address these limitations, the manufacturing industry has increasingly shifted towards non-traditional machining methods. These techniques enable contactless machining, minimizing tool-workpiece interaction and enhancing surface integrity. In the context of technological advancements, researchers have focused on smart materials with properties such as high resistivity, low corrosion susceptibility, and biocompatibility. These materials find extensive applications in the automation of industrial equipment, the biomedical field, aeronautics, and the automobile industry. Morphological analysis using scanning electron microscopy (SEM) and metallographic studies, such as assessing residual stress and micro-indentation hardness, play a critical role in evaluating machined surfaces. Efforts to reduce electrode damage caused by vibration and excessive heat propagation during machining have led to the development of advanced techniques. Parameters in electrical discharge machining (EDM) and wire electrical discharge machining (WEDM) are coupled with sensors for real-time monitoring of the machining zone and powder concentration. These innovations are supported by suitable modeling and metaheuristic techniques, including adaptive neuro-fuzzy inference systems (ANFIS), simulated annealing, teaching-learning-based optimization (TLBO), the finite element method (FEM), and the non-dominated sorting genetic algorithm II (NSGA-II). In some studies, it was found that the optimal settings for quantitative machining based on supervised controllable variables are pulse on time (125 µs), pulse on time (58 µs), peak current (11.5 A), servo voltage (55 V), and wire feed (2 mm/min). It also considered unsupervised variables such as vibration (0.133 kHz), surface roughness (0.133 μm), and overcut (3.70 μm) by the ANFIS approach. These approaches are implemented to optimize responses, enhance machining efficiency, improve surface integrity, and extend machine life cycles. This innovative methodology integrates real-time monitoring devices with machining tools or hard-to-machine workpieces, aiming to improve machining performance and material assessment. Additionally, the study analyzes hybrid nano powders, reviewing their historical background, recent technological advancements, and future potential applications. |
| format | Article |
| id | doaj-art-6ff31b38c04b4ebd9902ec55bbd4ab26 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-6ff31b38c04b4ebd9902ec55bbd4ab262025-08-20T03:03:25ZengSpringerDiscover Applied Sciences3004-92612025-07-017713910.1007/s42452-025-07385-4A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloysLokesh Kumar0Ashish Goyal1Vimal Kumar Pathak2Abhijit Bhowmik3Department of Mechanical Engineering, Manipal University JaipurDepartment of Mechanical Engineering, Manipal University JaipurDepartment of Mechanical Engineering, Manipal University JaipurDepartment of Additive Manufacturing, Mechanical Engineering, SIMATS, Saveetha Institute of Medical and Technical SciencesAbstract Conventional machining often results in excessive damage to machined equipment and the surfaces generated during the process. To address these limitations, the manufacturing industry has increasingly shifted towards non-traditional machining methods. These techniques enable contactless machining, minimizing tool-workpiece interaction and enhancing surface integrity. In the context of technological advancements, researchers have focused on smart materials with properties such as high resistivity, low corrosion susceptibility, and biocompatibility. These materials find extensive applications in the automation of industrial equipment, the biomedical field, aeronautics, and the automobile industry. Morphological analysis using scanning electron microscopy (SEM) and metallographic studies, such as assessing residual stress and micro-indentation hardness, play a critical role in evaluating machined surfaces. Efforts to reduce electrode damage caused by vibration and excessive heat propagation during machining have led to the development of advanced techniques. Parameters in electrical discharge machining (EDM) and wire electrical discharge machining (WEDM) are coupled with sensors for real-time monitoring of the machining zone and powder concentration. These innovations are supported by suitable modeling and metaheuristic techniques, including adaptive neuro-fuzzy inference systems (ANFIS), simulated annealing, teaching-learning-based optimization (TLBO), the finite element method (FEM), and the non-dominated sorting genetic algorithm II (NSGA-II). In some studies, it was found that the optimal settings for quantitative machining based on supervised controllable variables are pulse on time (125 µs), pulse on time (58 µs), peak current (11.5 A), servo voltage (55 V), and wire feed (2 mm/min). It also considered unsupervised variables such as vibration (0.133 kHz), surface roughness (0.133 μm), and overcut (3.70 μm) by the ANFIS approach. These approaches are implemented to optimize responses, enhance machining efficiency, improve surface integrity, and extend machine life cycles. This innovative methodology integrates real-time monitoring devices with machining tools or hard-to-machine workpieces, aiming to improve machining performance and material assessment. Additionally, the study analyzes hybrid nano powders, reviewing their historical background, recent technological advancements, and future potential applications.https://doi.org/10.1007/s42452-025-07385-4Shape memory alloyScanning electron microscopySensorsMachine learning techniques |
| spellingShingle | Lokesh Kumar Ashish Goyal Vimal Kumar Pathak Abhijit Bhowmik A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys Discover Applied Sciences Shape memory alloy Scanning electron microscopy Sensors Machine learning techniques |
| title | A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys |
| title_full | A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys |
| title_fullStr | A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys |
| title_full_unstemmed | A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys |
| title_short | A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys |
| title_sort | state of the art review of soft computing based monitoring and control in the machining of hard alloys |
| topic | Shape memory alloy Scanning electron microscopy Sensors Machine learning techniques |
| url | https://doi.org/10.1007/s42452-025-07385-4 |
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