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In order to survive in a fierce market scenario manufacturing industries are required to main‐tain high quality standards, produce at lowest cost, increase production rate, conserve resources and at the same time minimize the environmental impact of the processes they use. Machine tools are major pillars of any manufacturing system and are used on a large scale for processing of materials. However, machining processes are characterized by high energy consumption, high tool wear rate, poor surface quality and generation of large scale waste products in the form of used lubricants, coolants, dielectric or electrolytic fluids, chips and debris of tool or workpiece materials, etc. Thus, for success of any manufacturing system in terms of economy and to reduce its impact on the ecology it is crucial to improve the efficiency of these machine tools. Further‐more, in order to improve the sustainability of the process it is imminent that the machines are operated as efficiently as possible.

The performance of any machining process extensively depends upon the choice of process parameters. Therefore, for best performance from any machining process it is important to set the process parameters optimally. In order to determine the optimal setting of process parame‐ters it is important to map the relationship between input and output parameters. De Wolf et al.[1] investigated the effect of process parameters on material removal rate, electrode wear rate and surface finish in EDM process. Aich and Banerjee [2] applied teaching learning based opti‐