Abstract
The present work was performed to analyse the influences of machining parameters through analysis of variance (ANOVA) and to optimize the process parameters through Taguchi-fuzzy inference system (Fis) simulation during dry turning of cast A356 (Al–Si) aluminium alloy. The machining was done following Taguchi’s L9 orthogonal array at different speed, feed and depth of cut (DOC). The machining was done using a coated carbide tool. The depth of cut was found to be the highest contributing parameter as per analysis of variance. The depth of cut was also found as the most influential parameter according to the Taguchi-fuzzy inference system simulation. The analysis of variance showed significant contributions of all the machining parameters for material removal rate (MRR) maximization. The analysis of variance also indicated that speed had an insignificant influence for surface roughness (Ra) minimization. The optimal condition was noted at high speed, medium feed and high depth of cut. The thermal softening response of the alloy took place, and subsequently, continuous chips were formed at specific experimental condition. Discontinuous chips were formed in the other experimental conditions. Experimental observations also showed particle emission during the dry turning operation.
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Acknowledgements
The authors hereby gratefully acknowledge the assistance provided by NIT Agartala for material procurement. The authors also gratefully acknowledge the assistance provided by IIT Kharagpur during measurement of surface roughness in IIT Kharagpur.
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Pandey, V., Chakraborty, K. (2023). The Applications of ANOVA and Taguchi-Fis Simulation for Performance Evaluation of Machining Parameters and Subsequent Parametric Optimization During Dry Turning of A356 (Al–Si) Alloy. In: Manik, G., Kalia, S., Verma, O.P., Sharma, T.K. (eds) Recent Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-2188-9_1
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DOI: https://doi.org/10.1007/978-981-19-2188-9_1
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