Abstract
This paper presents the optimization and analysis of machining parameters on machining of Al6061 alloy using magnetic field-assisted powder-mixed electrical discharge machining (MFAPM-EDM) process. For performance analysis, peak current (IP), spark on time (SON), spark off time (SOFF), powder concentration (PC), and magnetic field (MF) are considered as machining parameters and tool wear rate (TWR) as performance measures. The experimental design based on Box Behnken Design is used for conducting the experiments and single objective optimization is performed using teaching–learning-based optimization (TLBO) algorithm. Peak current is observed as the most significant parameters followed by spark on time, magnetic field, and powder concentration. Model to predict TWR is developed in terms of machining parameters. The optimum set of machining parameters for minimum TWR using TLBO algorithm is obtained at IP-1 A, SON-30 µs, SOFF-51 µs, PC-10 g/l, and MF-0.45 T.
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Rouniyar, A.K., Shandilya, P. (2019). Analysis and Optimization of Tool Wear Rate in Magnetic Field-Assisted Powder-Mixed Electrical Discharge Machining of Al6061 Alloy Using TLBO. In: Narayanan, R., Joshi, S., Dixit, U. (eds) Advances in Computational Methods in Manufacturing. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-32-9072-3_42
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DOI: https://doi.org/10.1007/978-981-32-9072-3_42
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