A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

Abstract

Manufacturing processes are aimed at transforming materials into goods, generating wealth [1]. Camposeco-Negrete [2] states that cost and quality are the main goals of manufacturing companies. To improve quality in this type of process, several authors have studied the turning process using mathematical strategies in order to contribute to the efficiency of these processes, such as: the Taguchi method The primary input parameters in the turning process, i.e., cutting speed, feed rate and depth of cut [7], are directly responsible for the quality and productivity characteristics of the process, such as the amount of material removed, tool wear, and finishing of the product [8].Furthermore, the finishing of the machined parts can be evaluated according to the surface roughness, which are irregularities presented on the surface of the parts, characterized by grooves made by the tool during the machining process [9] of the cutting parameters (ranging from a single parameter per experiment) in the quality responses, such as tool life and surface roughness. This paper makes use of only one roughness parameter (arithmetic average roughness, R a ), considering the calculation of its metrics of the roughness characteristics (R a , R y , R q , R z and R t ). The average arithmetic roughness (R a ) is the arithmetic mean of the absolute values of the ordinates of the effective (measured) profile in relation to the midline in a sample length (Fig. 1). In addition, R a is the most used parameter for general quality control [10].The steel used in the turning process of this study was AISI 12L14 carbon steel (used in studies such as: Peruchi et al.

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