The constrained applications of conventional machining processes in generating complex shape ge-ometries with the desired degree of tolerance and surface finish in various advanced engineering materials are being gradually compensated by the non-traditional machining (NTM) processes. These NTM processes usually have higher procurement, maintenance, operating and tooling cost. Hence, in order to attain their maximum machining performance, they are usually operated at their optimal or near optimal parametric settings which can easily be determined by the application of dif-ferent optimization techniques. In this paper, 133 international research papers published during 2012-16 on parametric optimization of NTM processes are extensively reviewed to have an idea on the selected process parameters, observed responses, work materials machined and optimization techniques employed in those processes while generating varying part geometries for their industrial use. It is observed that electro discharge machining is the mostly employed NTM process, applied voltage is the identified process parameter with maximum importance, surface roughness and material removal rate are the two maximally preferred responses, different steel grades are the mostly machined work materials and grey relational analysis is the most popular tool utilized for para-metric optimization of NTM processes. These observations would help the process engineers to attain the machining performance of the NTM processes at their fullest extents for different work material and shape feature combinations.

Due to various added advantages over the conventional material removal processes, non-traditional machining (NTM) processes have now been widely applied in different manufacturing industries. To achieve the desired response values, it is always recommended to operate these NTM processes at their optimal parametric settings. Various single response optimization techniques are already available to determine the optimal combinations of NTM process parameters for achieving maximum or minimum value of a single response. In this paper, a multivariate quality loss function approach is adopted for simultaneous optimization of responses for three NTM processes. It is observed that this approach outperforms the other multi-response optimization techniques, like desirability function, distance function and mean squared error methods with respect to the achieved re-sponse values. With modification of the corresponding objective function and constraints of the de-veloped non-linear programming problem, it can be effectively applied to any non-traditional as well as conventional machining process as a multi-objective optimization tool.

Electrochemical micromachining (EMM) appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA) for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.

With ever-increasing demands for high surface finish and complex shape geometries on various difficult-to-machine materials, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove material from the workpiece surface. They are also cost effective for a wide range of micro- and nano-level applications. For effective utilization of different NTM processes, it is quite important to study their characteristics and material removal mechanisms in order to identify the most significant control parameters affecting the process responses. In this paper, a data mining approach using classification and regression tree algorithm is employed to identify the most important input parameters of three NTM processes, i.e. micro electro discharge milling process, wire electrical discharge machining process and laser beam machining process. The derived observations are also validated using the analysis of variance results.