Int J Adv Manuf Technol (2012) 58:885–894 DOI 10.1007/s00170-011-3455-7
ORIGINAL ARTICLE
Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718 J. S. Senthilkumaar & P. Selvarani & R. M. Arunachalam
Received: 28 January 2010 / Accepted: 13 June 2011 / Published online: 28 June 2011 # Springer-Verlag London Limited 2011
Abstract The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as
J. S. Senthilkumaar (*) Department of Mechanical Engineering, Podhigai College of Engineering and Technology, Tirupattur, Vellore, Tamil Nadu, India e-mail:
[email protected] P. Selvarani Department of Mechanical Engineering, Government Polytechnic College, Dharmapuri, Tamil Nadu, India e-mail:
[email protected] R. M. Arunachalam Department of Mechanical Engineering, Sona College of Technology, Salem, Tamil Nadu, India e-mail:
[email protected]
Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved. Keywords Inconel 718 . Flank wear . Surface roughness . Optimization . ANOVA . Artificial neural network . Turning . Facing
1 Introduction The recent developments in aerospace, petrochemical, marine, nuclear power generation, and process industries have driven the need for hard, high-strength and heatresistant super alloys (HRSAs). Both nickel-based and titanium-based alloys are suitable for aircraft engine components particularly rotating parts of gas turbines such as blades and disks because of their ability to maintain high strength even at elevated temperatures prevailing in the engine [1]. However, because of their high-strength abrasive nature, they pose problem in machining. The poor machinability of the HRSA is due to the various inherent properties namely: 1. Low thermal conductivity, 2. Rapid work hardening, 3. Ability to react with tool materials under atmospheric conditions, 4. Formation of built-up edge, 5. Weld to the cutting edges, and 6. Presence of abrasive carbides in their microstructure [2]. Among the HRSA, the nickel-based Inconel 718 is the most frequently used. Uncoated carbide tools are used to
886
machine nickel-based alloys at cutting speed range of 10– 30 m/min and at feed rates up to 0.5 mm/rev for improved productivity. Till date, carbides are mostly used as cutting tool material to machine Inconel 718 at slow speeds [3]. Earlier research indicated that uncoated tool performance was cost effective than the coated ones for finishing process. In the speed range of 26–48 m/min, no significant difference in tool life was observed for the coated and uncoated tools [4]. Even though high-speed machining significantly improves the productivity, it was limited by the sophisticated and old machines. Hence, the uses of uncoated carbide cutting tools are widely prevalent due to its economic considerations. The manufacture of aerospace components involves a variety of machining operations such as turning, facing, milling, and drilling. Considerable research has been done in turning and milling of Inconel 718 using coated and uncoated carbide cutting tools [4]. However, facing operation, although very important in the aerospace industry for the manufacture of disks, rings, casings, etc., has not attracted much attention [3, 5]. Many researchers are optimizing various machining process except facing process, since the process are very important as like as other machining processes. Parameter selection for facing process is choosing the right combination of cutting speed, feed, and depth of cut to achieve desired surface finish with minimum flank wear. In machining of parts, surface quality is one of most specified technical requirements in order to achieve proper assembly of machined components. The surface roughness is the major indicator of surface quality that directly relies on tool geometry like nose radius, edge geometry, rake angle, etc., which can be controlled by the tool manufacturer and machining parameters which need to be optimized [5]. In finish turning, tool wear becomes an additional parameter affecting surface quality of finished parts [6]. Flank wear directly influences the dimensions and quality of the surface [7, 8]. Flank wear can affect the fatigue endurance limit by affecting surface finish, lubrication retention capability by changing the distribution of heights and slopes of the surface, and other tribological aspects by affecting the topography of the machined surface. Therefore, the information about the state of flank wear is needed to plan tool changes and avoid scrap, or manipulate the feed and speed in-process to control tool life [9, 10]. In this work, the performance of uncoated carbide inserts of square geometry used for finish turning and facing of Inconel 718 is investigated. The guidelines for choosing cutting data provided by the cutting tool manufacturers are usually vague and insufficient to decide on quality and cost. The flank wear increased much faster in case of SNMM and other inserts than in SNMG (square) insert irrespective of the environments of machining [6]. Square inserts have been preferred to triangular inserts because they have
Int J Adv Manuf Technol (2012) 58:885–894
stronger geometry and cost effectiveness due to more cutting edges [11]. The main objective of this paper is to optimize the machining parameters for finish turning and facing of Inconel 718. The full-factorial experimental design approach is utilized for experimental planning, and analysis of variance (ANOVA) is employed to investigate the influence of machining parameters on the surface roughness and flank wear. The results obtained from the experimental study are utilized for analyzing and evaluating the effects of various input constraint at the optimal point. A new approach in genetic algorithm (GA) coupled with artificial neural network (ANN) for modeling and optimization has been adopted. In this research for GA-based optimization, the constraint evaluation has been developed using mathematical modeling and the fitness function using artificial neural network. The ANN models are proven to precisely solve the nonlinearity than the mathematical model, and hence, ANN models are used as fitness function; the mathematical models are used as constraints while limiting the machining parameter. The single-objective formulation is extended to reflect the nature of multi-objective problems (MOPs) where there is not one objective function to optimize but many. Thus, there is not one unique solution but a set of solutions. These sets of solutions are found through the use of Pareto optimality theory [12, 13]. Having two or more objective functions, the notion of “optimum” changes because in MOPs, the aim is to find good compromises (or “tradeoffs”) rather than a single solution as in global optimization. The notion of “optimum” most commonly adopted is generalized by Vilfredo Pareto. Pareto optimal solutions are those solutions within the genotype search space (decision space) whose corresponding phenotype objective vector components cannot be all simultaneously improved. These solutions are also termed non-inferior, admissible, or efficient solutions [14–16]. Here, the multi-objective optimization process generated the “non-dominated solutions” for minimization of surface roughness and minimization of flank wear. The graphs are plotted using the “nondominated solutions” called “Pareto optimal front,” for making the decision and selection of the machining parameters. The selected optimal machining parameters are experimentally validated.
2 Experimental procedure Single-pass finish turning and facing operations were conducted in dry cutting condition in order to investigate the performance and to study the wear mechanism of uncoated carbide tools on Inconel 718 in the form of cylindrical bar stock of diameter 38 mm. The experiments
Int J Adv Manuf Technol (2012) 58:885–894 Table 1 Chemical compositions of Inconel 718 (weight basis)
887
C
Si
Cu
Fe
Mn
Ti
Al
Cr
Mo
Ni
Co
Nb+Ta
0.034
0.07
0.04
Bal
0.09
0.98
0.48
17.40
2.98
50.80
0.04
5.294
were conducted on the L16 ACE designer lathe with following specifications: power, 7.5 kW motor drive; speed range, 0–3,500 rpm, and feed range, 0.01–1,000 mm/rev with constant speed capabilities. 2.1 Work material Inconel 718 material is used as the work material in the present investigation. The test specimens were prepared from the 38-mm cylindrical bar stock. Each specimen having 38 mm in diameter and 75 mm in length were used for turning and facing tests. The chemical composition and mechanical properties are given in Tables 1 and 2, respectively. 2.2 Tool material Comparative performance study had the same specification of cutting tool materials, but different tool holders were used for turning and facing processes. The cutting materials are K10 type uncoated carbide inserts, and as per ISO specification, inserts are designated as SNMG 120408-QM H13A, which are having the following tool geometry: inclination angle, −6°; orthogonal rake angle, −6°; orthogonal clearance angle, 6°; auxiliary cutting edge angle, 15°; principal cutting edge angle, 75°; and nose radius, 0.8 mm. Cutting tool inserts were clamped onto a tool holder with a designation of DSKNL 2020K 12 IMP for facing process and DBSNR 2020K 12 for turning process. 2.3 Machining parameters and levels Three levels were specified for each process parameter as given in the Table 3. The parameter levels were chosen within the intervals based on the recommendations by the cutting tool manufacturer. Three process parameters at three levels led to a total of 27 tests for finish turning and facing processes. Constant cutting length of 19 mm from the work piece for facing and turning was carried out for each experiment.
The machining operations were carried out on a 7.5-kW LT16 CNC lathe with a maximum spindle speed of 3,500 rpm. Three levels were specified for each process parameter as given in the Table 3. The parameter levels were chosen within the intervals recommended by the cutting tool manufacturer. A constant cutting length of 19 mm from the work piece for facing and turning was carried out for each experiment. Surface roughness was measured by Mittoyo-surftest 211 with sampling length of 0.25 mm. The flank wear was measured by Olympus Toolmakers microscope with 10× magnification and 1-μm resolution coupled with image processing software. 2.4 Design of experiments As mentioned earlier, three levels were specified for each of the factors as machining parameters (cutting speed, feed, and depth of cut). The standard orthogonal array L27 was chosen, which has 27 rows corresponding to the number of parameter combinations, with 26° of freedom and 13 columns at three levels (details not shown). The first, second, and fifth columns of the orthogonal array were assigned to cutting speed (V ), feed ( f ), and depth of cut, (a) and remains were assigned to the interactions. One test is performed for each combination and process, resulting in a total of 54 tests (27 tests for turning and 27 for facing process) without replications, which allows analysis of variance of the results. New cutting edge was used for each trial of experiment. The surface roughness was measured by positioning the stylus perpendicular to the feed marks on the machined surface towards the end of cutting. The surface roughness measurement was taken at four locations (90° apart) and repeated twice at each location on the face of the machined surface, and the average values were reported. Flank wear was measured at the end of the machining processes. Reducing costs in the cutting process together with reduced environmental pollution by the use of dry machining are the main keys for the industry to remain competitive and profitable in the future.
Table 2 Mechanical properties of Inconel 718 Tensile strength (MPa) 1,280
Yield strength (MPa)
Young modulus (MPa)
Density (kg/m3)
Melting point (°C)
Hardness (HRC)
Thermal conductivity (W/mK)
1,090
208×103
819
1,285
18
12.23
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Int J Adv Manuf Technol (2012) 58:885–894
Table 3 Machining parameters and levels Machining parameters Cutting speed V (m/min) Feed f (mm/rev) Depth of cut a (mm)
Level 1
Level 2
Level 3
25 0.1
35 0.15
45 0.2
1.0
1.25
1.5
variation. Equally from the analysis of Table 7, all the factors and their interactions do not have any statistical significance. Notice that the errors associated to the ANOVA table for the surface roughness were 25.79% (turning) and 13.21% (facing), and for the flank wear were approximately 9.98%(turning) and 31.8% (facing). 3.2 Development of mathematical model
3 Experimental results and data analysis 3.1 Analysis of variance The purpose of the statistical ANOVA is to investigate which design parameter significantly affects the surface roughness and flank wear. Based on the ANOVA, the relative importance of the machining parameters with respect to surface roughness and flank wear was investigated to determine the optimum combination of the machining parameters. Tables 4, 5, 6, and 7 show the results of the ANOVA analysis for the surface roughness and flank wear of turning and facing processes, respectively. This analysis was carried out for a significance level of α= 0.05, i.e., for confidence level of 95%. Tables 4, 5, 6, and 7 show that the probability levels are the realized significance levels, associated with the F tests for each source of variation. The sources with a probability level less than 0.05 are considered to have a statistically significant contribution to the performance measures. Also, last columns of Tables 4, 5, 6, and 7 show the percentage of contribution of each source to the total variation, indicating the degree of influence on the result. According to Table 4, all the factors and their interactions have insignificant effect on the surface roughness generation in turning process. Table 5 shows the significant factors and their contribution for the flank wear of the turning process which are cutting speed V (13.63%), depth of cut a (16%), and interactions V×a (25.22%) and f× a (20.94%), which explains altogether 75.79% of the total variation. From the analysis of Table 6, it is inferred that the only significant factor for surface roughness in facing process is feed f which explains 33.74% of the total Table 4 ANOVA table for surface roughness of turning process
Source term V f a V×f V×a f×a Error Total
Nonlinear regression models are another important and useful family of regression models. Nonlinear regression is a method of finding a nonlinear model of the relationship between the dependent variable and a set of independent variables. Unlike traditional linear regression, which is restricted to estimating linear models, nonlinear regression can estimate models with arbitrary relationships between independent and dependent variables [17, 18]. This is accomplished using iterative estimation algorithms. The proposed mathematical models of various responses are presented in the following form: Yi ¼ C V x1 f x2 ax3
ð1Þ
where C is the constant, V is the cutting speed, f is the feed, a is the depth of cut, and x1, x2, and x3 are estimated coefficients of regression model. Statistical simulation software estimates the parameters in nonlinear models using the Levenberg–Marquardt nonlinear least-squares algorithm. By using the machining parameters and responses from the experiments, the following exponential models for surface roughness and tool flank wear were developed and are given, respectively: Raturning ¼ 0:1161 V 0:3656 f 0:0984 a0:1323
ð2Þ
R2 ¼ 0:0585 VBturning ¼ 0:02597 V 0:5799 f 0:1797 a0:2201
ð3Þ
R2 ¼ 0:2084
Degree of freedom
Sum of squares
Mean square
2 2 2 4 4 4 8 26
0.07061 0.00501 0.03645 0.14395 0.3279 0.15617 0.25721 0.9973
0.0353 0.0025 0.01823 0.03599 0.08198 0.03904 0.03215
F ratio
Probability level
% of contribution
1.1 0.08 0.57 1.12 2.55 1.21
0.379 0.926 0.589 0.412 0.121 0.376
7.08 0.50 3.65 14.43 32.88 15.66 25.79 100.00
Int J Adv Manuf Technol (2012) 58:885–894 Table 5 ANOVA table for flank wear of turning process
Source term
889 Degree of freedom
Sum of squares
Mean square
Probability level
% of contribution 13.63
V
2
0.0080401
0.00402
5.46
0.032
f
2
0.0005676
0.0002838
0.39
0.692
0.96
a V×f
2 4
0.009435 0.0078233
0.0047175 0.0019558
6.41 2.66
0.022 0.112
16.00 13.27
V×a f×a
4 4
0.0148739 0.0123477
0.0037185 0.0030869
5.05 4.2
0.025 0.04
25.22 20.94
Error
8
0.0058861
0.0007358
Total
26
0.0589736
Rafacing ¼ 4:5774 V 0:2766 f 0:6342 a0:4281
ð4Þ
R2 ¼ 0:4464 VBfacing ¼ 0:048 V 0:2751 f 0:0314 a0:1713
ð5Þ
R2 ¼ 0:0945 The R2 value of the model is always greater than 0 and less than 1 (0
