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A Data Mining Approach for Analysis of a Wire Electrical Discharge Machining Process

Dandge Shruti Sudhakar, Chakraborty Shankar

Management and Production Engineering Review

2021

Vol. 13, No. 3

116--128

Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.

Groups of too close radiocarbon dates

Walanus A.

Geochronometria

2006

Vol. 25

5-10

In the archaeological and palaeogeographical literature, it is relatively frequent to find groups of radiocarbon dates of too close values. Too close means here that the dates exhibit no other variability than that of random origin, quantified by the given measurement errors. The chi-squared statistic seems to be appropriate to test, if the given group of dates is of random variability, of larger variability (what is typical) or of too small variability. The last case is hard to explain.