Fuzzy linguistic optimization on surface roughness for CNC turning.(Research Article)(Report)

1. Introduction

Machining operations have been the core of the manufacturing industry since the industrial revolution [1]. The existing optimization researches for computer numerical controlled (CNC) turning were either simulated within particular manufacturing circumstances [25] or achieved through numerous frequent equipment operations [6,7]. Nevertheless, these are regarded as computing simulations, and the applicability to real-world industry is still uncertain. Therefore, a general deduction optimization scheme without equipment operations is deemed to be necessarily developed.

The machining process on a CNC lathe is programmed by speed, feed rate, and cutting depth, which are frequently determined based on the job shop experiences. However, the machine performance and the product characteristics are not guaranteed to be acceptable. Therefore, the optimum turning conditions have to be accomplished. It is mentioned that the tool nose runoff will affect the performance of the machining process [8]. Therefore, the tool nose runoff is also selected as one of the control factors in this study.

Parameter optimization is a hard-solving issue because of the interactions between parameters. Problems related to the enhancement of product quality and production efficiency can always be related to the optimization procedures. Taguchi method, an experimental design method, has been widely applied to many industries. It can not only optimize quality characteristics through the setting of design parameters but also reduce the sensitivity of the system performance to sources of variation [9-12]. The Taguchi method adopts a set of orthogonal arrays to investigate the effect of parameters on specific quality characteristics to decide the optimum parameter combination. These kinds of arrays use a small number of experimental runs to analyze the quality effects of parameters as well as the optimum combination of parameters.

To achieve the general optimization, it is necessary to first describe the dynamic behavior of the system to be controlled. Because of the number, complexity, and unclear, vague nature of the variables of the dynamic systems that may influence the decision maker's decision, fuzzy set theory is the most suitable solution [13,14]. Fuzzy linguistic models permit the translation of verbal expressions into numerical ones [15]. Therefore, the input-output relationship of the process can be described by the collection of fuzzy control rules involving linguistic variables rather than a complicated dynamic mathematical model.

With all the viewpoints above, this paper considers four parameters (cutting depth, feed rate, speed, and tool nose runoff) with three levels (low, medium, and high) to optimize surface roughness in CNC finish turning. The fuzzy control rules using triangle membership function with respective to five linguistic grades for surface roughness are additionally constructed. The defuzzification is then quantified using center of gravity and introduced to Taguchi experiment. Thus, the optimum fuzzy linguistic parameters can then be received. This paper definitely proposes a general deduction optimization approach and satisfactory fuzzy linguistic technique for improving surface roughness in CNC turning with profound insight.

2. Methodology

In this paper, the linguistic variable quantification and parameter optimization for CNC turning operations are proposed using fuzzy set theory and Taguchi method, respectively. They are described as below.

2.1. Fuzzy Set Theory

Let X be a universe of discourse; [??] is a …