5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India
Application of grey fuzzy logic for simultaneous optimization of surface roughness and metal removal rate in turning Al-SiCp metal matrix composites Santosh Tamang1 and M. Chandrasekaran*2 Department of Mechanical Engineering, NERIST, Nirjuli, Arunachal Pradesh, INDIA 791109, 1 *2 Email:
[email protected] ,
[email protected]
Abstract Metal matrix composites (MMCs) are difficult to machine due to the presence of hard abrasive reinforcement materials. The selection of optimum machining parameters is essential for economic production of quality components. The present investigation focuses on finding the optimal turning parameters considering multiple performance characteristics using grey fuzzy logic approach. Taguchi’s L27 orthogonal array of experiments was performed in turning Al-SiCp MMC using poly crystalline diamond (PCD) tool. Two important performance measures i.e., surface roughness (Ra) as a parameter for job quality and material removal rate (MRR) for economic production of the components were optimized. The grey output is fuzzified into eight membership functions and 27 rules were developed. The highest grey fuzzy reasoning grade (GFRG) obtained using MATLAB 7.10® tool box shows the grade improvement of 0.12 in comparison with grey relational grade (GRG). The proposed grey fuzzy logic approach found more effective to evaluate the multiple performance characteristics and simplifies the optimization procedure in optimizing complicated process responses. Keywords: Grey fuzzy logic, MMC, Machining, Optimization
1 Introduction The metal matrix composites (MMCs) are found as the most commonly used material because of its improved properties in comparison with nonreinforced alloys. Among these aluminium alloy (Al) with silicon carbide (SiC) particulate reinforcement MMC is used for manufacturing components in automotive and aerospace industries. The economic production of quality products is prime need for world competitiveness. Therefore, optimization of process parameters for two important characteristics viz., minimizing surface roughness and maximizing material removal rate (MRR) is essential manufacturing industries. Since improvement of one response may degrade another response, simultaneous optimization of both the parameters is carried out. Recently, grey relational analysis (GRA) and desirability function analysis (DFA) are used by number of researchers for optimization of multiple performance characteristics in various machining processes by Lin et al. (2000) and Muthukrishnan et al. (2012). The grey relational analysis developed based on the grey system theory by Deng (1898) is used for solving the complicated interrelationships among the multiple responses. The fuzzy-based Taguchi method is used to optimize the multi-response process
through the settings of process parameters. In fuzzy logic the ‘max–min’ fuzzy inference and centroid defuzzification methods have been applied for dealing multiple responses. Researchers have attempted to optimize different machining parameters viz., spindle speed (N), feed (f) and depth of cut (d) on the surface roughness (Ra) and material removal rate, tool wear (VB), tool life etc. using different optimization techniques such as Taguchi grey relational analysis, fuzzy logic, genetic algorithm, particle swarm optimization etc. In this paper, the use of grey relational analysis and the fuzzy-based Taguchi method is used for optimizing the turning process in machining Al-SiCp composites using polycrystalline diamond tool (PCD). Lin et al. (2002) employed grey relational analysis for optimizing the multiple responses in EDM process while machining SKD11 alloy steel of 12 mm diameter. Electrode wear ratio, material removal arte and surface roughness were considered as process responses. They found that grey relational technique is more straightforward and simple than grey fuzzy for optimizing multi response problems. Palanikumar et al. (2006) performed experiment on turning GFRP composites using carbide (K10) tool. They considered fiber orientation angle, cutting speed, feed rate, depth of cut and machining time as input parameters for measuring material removal rate,
832-1
Application of grey fuzzy logic for simultaneous optimization of surface roughness and metal removal rate in turning Al-SiCp metal matrix composites
tool wear and surface roughness. Taguchi method with fuzzy logic is used to optimize the responses simultaneously and found that at fiber orientation of 60°, cutting speed of 175 m/min, feed of 0.10mm/rev, depth of cut of 1.5 mm and machining time for 3 min the material removal rate increased from 15000 to 26250 mm3/min. The tool wear was reduced from 0.62 to 0.49 mm and the surface roughness was improved from 5.58 to 4.22 µm. They conclude that Taguchi with fuzzy logic is more convenient and useful technique for multi response optimization. Krishnamoorthy et al. (2012) applied grey fuzzy logic for multi response optimization in drilling CFRP composite. They had taken input as Spindle speed, point angle and feed are input parameters and responses are thrust force, torque, entry delamination, exit delamination and eccentricity of the holes. They conclude that high spindle speed (3000 rpm), low point angle (100°) and low feed rate (100 mm/min) is the optimum parameters level for drilling CFRP composites. Palanikumar et al. (2012) applied Taguchi method with Grey-fuzzy logic for simultaneous optimization of material removal rate, surface roughness, and specific cutting pressure in machining glass fiber reinforced plastic (GFRP) composite. They found that the technique is highly useful for optimizing multi performance characteristics. Rajmohan et al. (2013) conducted experiments on drilling hybrid composite Aluminium alloy (Al 356) using TiN coated and HSS drill and optimizes thrust force, surface roughness and burr height simultaneously using grey fuzzy logic. Feed rate and SiC% are most influencing parameters. Tamang and Chandrasekaran (2013) applied Taguchi grey relational analysis for optimizing surface roughness and tool wear in turning Al/SiCp MMC. They found that the experimental verification of optimum parameters obtained as v=100 m/min, f= 0.3 mm/rev and d=0.5 mm provide the percentage error of 1.03 and 5.5 for surface roughness and tool wear respectively. The review of literatures reveals that the researchers are mainly focused in optimizing multi responses characteristics. Grey fuzzy is recently employed by many researchers in optimizing conventional and non conventional machining processes such as milling, grinding, turning, drilling, EDM etc. In this work, Taguchi’s L27 orthogonal array of experiments was performed in turning Al-SiCp MMC using PCD tool. Three process parameters viz., spindle speed, feed and depth of cut were used to investigate two important performance measures i.e., surface roughness and material
removal rate. An optimal combination of parameters that minimize Ra and maximize MRR is obtained using grey fuzzy logic approach.
2 Experimental works Turning of Al-SiCp (10%) MMC was performed using poly crystalline diamond (PCD) tool. Taguchi’s L27 orthogonal array of experimental design was followed. Spindle speed (N), feed (f) and depth of cut (d) were considered as process parameters and their levels respectively being 500, 775, 1200 rpm, 0.11, 0.22, 0.44 mm/rev and 0.5, 0.75, 1.0 mm was used. The machining experiments were carried out on lathe: Kirloshkar make, Model: M1330. The workpiece diameter is 60 mm and machining length is 150 mm. Machining were carried out at different set of cutting conditions. The CLA value of surface roughness was obtained in each experimental trial. The work piece, Al-SiCp MMC (5% by weight) and PCD tool is shown in Fig.1.The surface roughness (Ra) is measured using Pocket Surf (Mahr, GMBH) surface roughness measuring instrument. Its measuring range was 0.03–6.35 mm.
Figure 1 Work piece and cutting tool. For the productivity evaluation material removal rate was evaluated using empirical relation given by the Eq.1. MRR=1000vfd mm3/min (1) (or) MRR = πDNfd,
(2)
where N is spindle speed (rpm), v is cutting speed (m/min), D is diameter of the workpiece (mm), f is feed (mm/rev) and d is depth of cut (mm). The experimental responses obtained are presented in Table 1.
832-2
5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India
3 Simultaneous Optimization of Ra and MRR Among various process responses the optimization of machining parameters for single objective is not appropriate for the other responses. Therefore the optimization multi-response characteristics have become important for manufacturing industries. In this work, simultaneous optimization of (i) surface roughness (Ra) being one of the parameter of product quality and (ii) material removal rate being economic aspects of the production process are considered to optimize the process using grey fuzzy system. 3.1 Grey relational analysis In GRA, the optimization of multiple response characteristics is converted into single grey relational grade. The procedure involves: (i) conversion of experimental data into normalized values, (iii) evaluation of grey relational coefficients and (iv) generating grey relational grading. In this work it is decided to optimize simultaneously Ra and MRR. Experimental data sets based on full factorial design 33=27 data sets are used. The response values are normalized to Zij (i.e., 0
