Development of association rules to study the parametric influences in non-traditional machining processes

Abstract

Non-traditional machining (NTM) processes have already emerged out as the suitable substitutes for the conventional metal removal methods due to their capability of generating complicated shape geometries on diverse difficult-to-machine engineering materials. For these NTM processes, it is always proposed that they should be operated while setting their various input parameters at the optimal levels for achieving better machining performance. In this paper, the application of a data mining tool in the form of development of association rules is explored to determine the best machining conditions for three NTM processes, i.e. electrochemical machining process, ultrasonic machining process and electrical discharge machining process. These rules, presented as simple ‘If-Then’ statements, would also guide the concerned process engineers in investigating the effects of various NTM process parameters on the considered responses. It is observed that the most preferred parametric combinations identified based on the generated association rules closely match with those as perceived by the past researchers.