# Optimization of Machining Parameters Using Fuzzy Taguchi Method for Reducing Tool Wear
## 1 Introduction  
For the increasing production costs and restrictive international laws regarding environmental protection, manufacturing industries are gradually transferring to green production as well as actively developing new processes that conserve energy, consume less resources, and generate less waste. These measures have substantially reduced production cost in not only assisting entrepreneurs to fulfill the social responsibility of the corporation but also meeting sustainable management objectives.  
Precision turning is a universal and fundamental process in metal cutting. Since the demand for high quality products is increasing, the turning tools used in computer numerical controlled (CNC) machines are critical and irreplaceable for high-precision metal parts. The parameter setting of CNC machining operations often relies on practical experience. In some cases, the parameters are even selected using trial and error that often leads to increased tool costs and the production time; therefore, establishing a prospective and versatile optimal parameter analyzing method is deemed to be necessary for metal cutting industries.  
In the existing studies on tool wear, researchers have focused on topics such as employing ceramic tools to cut Inconel 625 and using analysis of variance to determine the effects of cutting parameters on material removal rate and tool wear. It was found that cutting depth was the primary factor affecting tool wear [1]. A two-step method [2] was also proposed for predicting the lifetime of tools during cutting processes. It was found that tool life can be estimated and the production cost can be reduced by using image recognition software and artificial neural network (ANN). The Taguchi Method was also employed to select cutting parameters (tool nose radius, cutting speed, feed rate, and cutting depth)for optimizing tool wear and surface roughness in cutting AISI 4340 steel. The result revealed that the optimal cutting parameters could reduce production cost in the automatic production system [3].  
An experimental study [4] in turning mild-steel round bar by using various combinations of federate, cutting depth, and speed was also developed. Through the scanning electron microscope (SEM), the tool wear can be measured. It was found that larger cutting depth and higher speed would increase the cutting temperature that will shorten the tool life. The parameter optimization using grey relational analysis and TOPSIS has been studied in turning Mg alloy with polycrystalline diamond (PCD) tools [5]. The mathematical model was constructed by using Response Surface Analysis (RSA) and then verified by using Analysis of Variance (ANOVA). It was found that the federate and speed were major factors to the surface roughness and tool wear [5]. The MATLAB software (R2016a, Yuan Ze University, Taoyuan, R.O.C., Taiwan, 2016) was also used to build mathematical models in predicting tool wear during turning [6]. The above studies are all under a specific environment using certain equipment. These studies have to be achieved through the operation of process equipment to verify the result of tool wear that often increase the cost. Thus, the semantic rules to quantify and calculate the optimum parameters without operating the equipment will surely enhance the competitiveness for the precision CNC turning industry.