Treffer: A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys.

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. [ABSTRACT FROM AUTHOR]

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