## 1 Introduction  
Precision turning plays an important role in manufacturing industry. The products made by turning process include automotive components, aerospace parts, and precise industrial parts, _etc._ Turning accuracy is mainly influenced by the accuracy of the machine, the condition of the cutter, cutting parameters, and environmental conditions, such as external vibration, the environment temperature, _etc._ Since tool wear could cause more cutting resistance, machining vibration, machining temperature, and machining errors, it usually needs on-line monitoring of the status of tool wear and compensating of the tool wear via offsetting the tool position for the next machining process. To receive a better performance from a machining process, [PERSON] _et al._ carried out a series of research activities [1; 2] on improving the multi-gene genetic programming approach. They proposed a modified multi-gene genetic programming (M-MGGP) method using a stepwise regression approach in which the lower-performance genes were eliminated and the high-performing genes were combined. Validation was carried out by applying surface roughness to modeling when turning hardened American Iron and Steel Institute (AISI) H11 steel, and the results showed that M-MGGP has better performance than that of standard MGGP and other methods. They also proposed a new complexity-based multi-gene genetic programming approach in which the functional relationships between the energy consumption and the input process parameters of a milling process were obtained to find an optimum set of input settings; this will conserve a greater amount of energy from these operations. To improve the generalization ability of MGGP, [PERSON] _et al._[3] developed a new ensemble-based MGGP framework that used statistical and classification strategies. The method was applied on the reliable experimental database in which the outputs were surface roughness, tool life and power consumption. With the use of an embedded approach to molecular dynamics and MGGP, [PERSON] _et al._[4] proposed a method to investigate the thermal property of single-layer graphene sheet. In this study, the response of thermal conductivity of the graphene sheet with changes in system temperature and Stone-Thrower-Wales (STW) defect concentration was analyzed. In addition, they [5] also used an explicit model formulated by a molecular dynamics-based computational intelligence approach and a paradigm of a computational intelligence (CI) cluster comprising genetic programming to study the nano-drilling process of boron nitride nanosheet panels. Much research related to tool wear prediction and monitoring had been carried out in past years. [PERSON] _et al._[6] proposed a method of cutter wear prediction for certain cutting condition, and conducted verification cutting experiments. With the use of different sensor signals, [PERSON] _et al._[7] proposed a monitoring method of cutter wear. [PERSON] _et al._[8] proposed methods for cutter wear inspection and failure diagnosis, which could predict the influence of surface quality of the machined work piece caused by cutter wear. [PERSON] _et al._[9] developed an intelligent monitoring system which could provide on-line monitoring of the wear condition of the turning tool. [PERSON] _et al._ studied [10] the ways for monitoring the condition of end mills. With the use of the neural network method, [PERSON] _et al._[11] proposed a way to predict the dimension errors caused by cutting force and radial cutting vibration. [PERSON] _et al._[12] used a back-propagation neural network (BPN) to predict the status of tool wear to avoid cutter breakage. In order to meet the requirements of tight tolerance and high production yield rates for production, in-process tool wear monitoring and compensation and in-process quality inspection are usually developed and become a part of the manufacturing process.