Abstract
Super-duplex stainless steels (SDSSs), the second generation duplex stainless steels (DSSs), provide an excellent combination of high mechanical strength, high toughness, and good corrosion resistance. However, due to high levels of various alloying elements, machinability of SDSSs is very poor. In this study, machinability of SDSS SAF 2507 is discussed for turning operation under varying machining conditions. Temperature is measured for a range of cutting speeds under both dry and wet conditions. The techniques of response surface methodology (RSM) and artificial neural network (ANN) are used to obtain and compare predictive models for surface roughness. Optimization of cutting parameters is done using Genetic Algorithm (GA) to obtain maximum surface finish. From the results obtained, feed rate was found to be the most significant factor for surface roughness. Flank wear is studied after a fixed time of turning for various cutting speeds, and it was seen that it increased significantly with increase in cutting speed.
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Subhash, N., Sambedana, S., Nithin Raj, P., Jagadeesha, T. (2019). Experimental Study on Tool Wear and Optimization of Process Parameters Using ANN-GA in Turning of Super-Duplex Stainless Steel Under Dry and Wet Conditions. In: Hiremath, S., Shanmugam, N., Bapu, B. (eds) Advances in Manufacturing Technology. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6374-0_47
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DOI: https://doi.org/10.1007/978-981-13-6374-0_47
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