• SHANKAR CHAKRABORTY

      Articles written in Sadhana

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

      SUBHAM AGARWAL SHRUTI SUDHAKAR DANDGE SHANKAR CHAKRABORTY

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      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 investigatingthe 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.

    • Grey-fuzzy method-based parametric analysis of abrasive water jet machining on GFRP composites

      VIDYAPATI KUMAR PARTHA PROTIM DAS SHANKAR CHAKRABORTY

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      Abrasive water jet machining (AWJM) is an advanced non-traditional material removal process which can machine almost all types of thin hard-to-cut materials. The quality of its machining operation can be effectively enhanced while selecting the appropriate settings of its different input parameters through the application of optimization techniques. This paper aims in obtaining the optimal combination of four AWJM control parameters, such as water jet pressure, stand-off distance, abrasive mass flow rate and traverse speed while machining of glass fiber reinforced polymer (GFRP) composites. Grey relational analysis combined withfuzzy logic is employed here for attaining the most favored values of the process outputs (responses), i.e material removal rate, surface roughness, kerf width and kerf angle. The effects of varying AWJM process parameters on the measured responses are further studied through the developed interaction plots, while thecontributions of those process parameters are identified through analysis of variance technique. The response surface plots would further help in determining the attainable values of the corresponding process parameters to realize the desired quality of the considered responses

    • Structural equation modeling-based performance estimation and parametric analysis of wire electrical discharge machining processes

      BANESWAR SARKER SHANKAR CHAKRABORTY

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      This paper deals with development of structural equation models for two wire electrical discharge machining (WEDM) processes so as to investigate and estimate their machining performance along with identification of the most significant input parameters influencing the operational achievement of the said processes. These models also search out the corresponding responses to be treated as the best performance indicators for both the WEDM processes. Based on the experimental and simulated datasets, two models are subsequently developed showing the relationships between the input parameters, responses and machining performance for each of the considered WEDM processes. For both the processes, it is observed that the second model, developed without considering the insignificant input parameters, has better goodness-of-fit value as compared to the first model where all the WEDM process parameters are contemplated as the predictor variables. It can be revealed that pulse-on time is the most important predictor of machining performance with standardized coefficients of 0.61 and 0.75 respectively for the two WEDM processes. Machining rate acts as the main indicator variable in the first experimental dataset, while gap current is the most significant indicator variable in the second experimental dataset with factor loadings of 0.98 and 0.92 respectively

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