Optimization of 6061T6 CNC Boring Process Using the Taguchi Method and Grey Relational Analysis

This study investigated the optimization of computer numerical control (CNC) boring operation parameters for aluminum alloy 6061T6 using the grey relational analysis (GRA) method. Nine experimental runs based on an orthogonal array of Taguchi method were performed. The surface properties of roughness average and roughness maximum as well as the roundness were selected as the quality targets. An optimal parameter combination of the CNC boring operation was obtained via GRA. By analyzing the grey relational grade matrix, the degree of influenced for each controllable process factor onto individual quality targets can be found. The feed rate is identified to be the most influence on the roughness average and roughness maximum, and the cutting speed is the most influential factor to the roundness. Additionally, the analysis of variance (ANOVA) was also applied to identify the most significant factor; the feed rate is the most significant controlled factor for the CNC boring operations according to the weighted sum grade of the roughness average, roughness maximum and roundness. INTRODUCTION Boring is a precision machining process for generating internal cylindrical forms by removing metal with singlepoint tools or tools with multiple cutting edges. This process is most commonly performed with the workpiece held stationary and the cutting tool both rotating and advancing into the work. Common applications for boring include the enlarging or finishing of cored, pierced, or drilled holes and contoured internal surfaces. Related operations sometimes performed simultaneously with boring include turning, facing, chamfering, grooving, and threading. Precision boring can be performed on machines specifically designed for this purpose. In general, these machines relatively use light cuts, maintain close tolerances, and are often capable of high production rates. Additionally, machining centers (MC) have been defined as CNC machines with automatic tool-changing capabilities and rotating cutting tools. Increased productivity and versatility are major advantages of MC. The ability to perform drilling, turning, reaming, boring, milling, contouring, and threading operations on single machine eliminates the need for a number of individual machine tools, thus reducing capital equipment and labor requirements. Therefore, aluminum alloy 6061T6 boring process is done on the various cutting parameters of MC and is discussed in this study. The current boring operation can increase productivity if compared with a grinding operation. The boring process parameters such as cutting tool geometry and materials, depth of cut, feed rates, cutting speeds impact the material removal rates and the machining quality such as surface roughness and roundness has been studied intensively. *Address correspondence to this author at the Minghsin University of Science and Technology, 1 , H s in H s in g Ro a d , H s in Fe n g , 3 0 4 , H s in c h u , Taiwan; Tel: +886-3-5593142 ext.3001; Fax: +886-3-5782822, E-mail: yky@must.edu.tw Recently, Deng (1989) [1] proposed a grey relational analysis (GRA). The GRA is a method for measuring the degree of approximation among the sequences using a grey relational grade. Theories of the GRA have attracted considerable interest among researchers. Some other researchers have also examined the optimization of process parameters. For example, Huang and Lin (2002) [2] applied the GRA to design the die-sinking EDM machining parameters. Fung et al. (2003) [3] studied the GRA to obtain the optimal parameters of the injection molding process for mechanical properties of yield stress and elongation in polycarbonate/acrylonitrile-butadiene-styrene (PC/ABS) composites. Shen et al. (2004) [4] studied different polymers (such as PP, PC, PS, POM) with various process parameters of the micro-gear. The simulation used Taguchi method and GRA was provided. An employed the Taguchi method and the GRA to optimize the turning operations with multiple performance characteristics by Lin (2004) [5]. A used the GRA to optimize of the wire electric discharge machining process of particle-reinforced material with multiple performance characteristics by Chiang and Chang (2006) [6]. Yang et al. (2006) [7] also applied the Taguchi method and the GRA to optimize the dry machining parameters for high-purity graphite in end milling process, etc. Planning the experiments through the Taguchi orthogonal array has been used quite successfully in process optimization by Chen and Chen (2007) [8], Fung and Kang (2005) [9], Tang et al. (2007) [10], Vijian and Arunachalam (2006) [11], Yang (2007) [12] as well as Zhang et al. (2007) [13], etc. Therefore, this study applied a Taguchi L9 (3 4 ) orthogonal array to plan the experiments on CNC boring operations. Three controlling factors including the cutting speed, the feed rate, and the depth of cut with three levels for each factor were selected. The GRA is then applied to examine how the CNC boring operation factors influence the quality targets of roughness average, roughness maximum and roundness. An optimal parameter combination was then Optimization of 6061T6 CNC Boring Process The Open Industrial and Manufacturing Engineering Journal, 2009, Volume 2 15 obtained. Through analyzing the grey relational grade matrix, the most influential factors for individual quality targets of the CNC boring operations can be identified. Additionally, the ANOVA was also utilized to examine the most significant factors for the CNC boring process as the roughness average, roughness maximum and roundness are simultaneously considered. GREY RELATIONAL ANALYSIS Data Preprocessing Grey data processing must be performed before grey correlation coefficients can be calculated. A series of various units must be transformed to be dimensionless. Usually, each series is normalized by dividing the data in the original series by their average. Let the original reference sequence and sequence for comparison be represented as x0(k) and xi(k), i=1, 2, ..., m; k=1, 2, ..., n, respectively, where m is the total number of experiment to be considered, and n is the total number of observation data. Data preprocessing converts the original sequence to a comparable sequence. Several methodologies of preprocessing data can be used in grey relation analysis, depending on the characteristics of the original sequence (Deng, 1989 [1]; Gau et al., 2006 [14]; You et al., 2007 [15]). If the target value of the original sequence is ‘‘the-larger-the-better’’, then the original sequence is normalized as follows, xi * k ( ) = xi (0) k ( ) min.xi (0) k ( ) max.xi (0) k ( ) min.xi (0) k ( ) (1) If the purpose is ‘‘the-smaller-the-better’’, then the original sequence is normalized as follows, xi * k ( ) = max.xi (0) k ( ) xi (0) k ( ) max.xi (0) k ( ) min.xi (0) k ( ) (2) However, if there is ‘‘a specific target value’’, then the original sequence is normalized using, xi * k ( ) =1 xi (0) k ( ) OB max. max.xi (0) k ( ) OB,OB min.xi (0) k ( ) { } (3) where OB is the target value. Alternatively, the original sequence can be normalized using the simplest methodology that is the values of the original sequence can be divided by the first value of the sequence, xi (0) (1). xi * k ( ) = xi (0) k ( ) xi (0) 1 ( ) (4) where xi (0) k ( ) : the original sequence ( ) k x i * : the sequence after the data preprocessing max. xi (0) k ( ) : the largest value of xi (0) k ( ) min. xi (0) k ( ) : the smallest value of xi (0) k ( ) Grey Relational Coefficients and Grey Relational Grades Following the data preprocessing, a grey relational coefficient can be calculated using the preprocessed sequences. The grey relational coefficient is defined as follows. x0 * k ( ), xi * k ( ) ( ) = min . + max . 0i k ( ) + max . 0< x0 * k ( ), xi * k ( ) ( ) 1 (5) where 0i k ( ) is the deviation sequence of reference sequence x0 * k ( ) and comparability sequence ( ) k xi * , namely 0i k ( ) = x0 * k ( ) xi * k ( ) max . = max. j i max. k x0 * k ( ) x j * k ( ) min . = min. j i min. k x0 * k ( ) x j * k ( ) : distinguishing coefficient, 0,1 [ ] A grey relational grade is a weighted sum of the grey relational coefficients, and is defined as follows. x0 , xi * ( ) = k x0* k ( ), xi* k ( ) ( ) k=1 n