Affiliation:
1. Advanced Technologies in Mechanical Production Research Laboratory (LRTAPM), Badji Mokhtar — Annaba University, P.O. Box 12, 23000 Annaba, Algeria
2. Electrotechnic laboratory of Constantine (LEC), University of Frères Mentouri Constantine 1, Constantine 25001, Algeria
Abstract
Quality of products is a major concern for hard machining industry aspects. However, the prediction of surface roughness in the function of the machining parameters is a targeted objective by the majority of researchers due to important industrial interest. This work is a contribution for developing comprehensive analyses of surface roughness models during dry hard turning of cold work steel AISI D2. It is focused on the treatment of predicted surface roughness values in correlation with cutting conditions such as cutting parameters (cutting speed, feed rate, and depth of cut), tools material, tool geometry, workpieces hardness, and machining times in order to establish of orientations for use which applied successfully in the industry from optimal selection of process variables. The approach consists in first conducting a literature survey through collecting and validating surface roughness models together with the corresponding material and also the cutting conditions. Then, results have been tabulated as to permit data mining treatment under data processing software which was developed. As a significant result, a strategic dashboard generating roughness data mining was made available to researchers and industrials. The optimums of parameters which condition the achievement of best machined surface qualities have been selected.
Publisher
World Scientific Pub Co Pte Ltd