Optimization of Laser Beam Machining Process Parameters of HSLA Steel Using MOORA
D. Vijay PraveenP. UmamaheswarraoAvula SureshShaik MusharafPraveen SaravananS. AbdullaT. Sahit Kumar
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The current research paper is focused on investigating the influence of Laser beam machining process parameters on surface roughness and kerf width of HSLA steel. Taguchi’s L 18 orthogonal array is adopted to conduct the machining studies. MOORA method is used to evaluate the suitable combination of the LBM process parameters. The combined effect of machining performance measures is analysed using analysis of variance to identify the significance of the result. Consequently, the influence of the parameters on machining responses were explored. The surface morphology of the machined surface of the optimal set of parameters has been studied.Keywords:
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Taguchi optimization method is a statistical technique to optimize the selected factors and will improvise the quality of compositions. The aim of this paper is to define effective optimization techniques to identify the effective orthogonal array combination using experiments in the beam structure. The Taguchi L9 array has experimented in this study with three different levels and four parameters. After the completion of the experiments, the results are compared with fully factorial methods. The output will be in the form of S/N ratio and graphs. The best-optimized combination is found for minimizing the number of experiments. The size of the beam structure is 1250mm*150mm*150mm.
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In the present work, a Design Of Experiment (DOE) technique, the Taguchi method was employed to bead on welding trials to optimize Pulsed Current Gas Tungsten Arc (PCGTA) welding process parameters of alloy C-276. A L9 orthogonal array of Taguchi design involving nine experiments for four parameters (pulsed current, background current, % on time, pulse frequency) with three levels was used. An analysis of the mean of signal-to-noise (S/N) ratio indicates that the depth of current is influenced significantly by the levels in the Taguchi orthogonal array. The higher the better response category was selected to obtain optimum conditions for depth of penetration. The optimum conditions were found to be 165 A pulse current, 77 A background current, 60% on time and 5 Hz pulse frequency. Analysis of Variance (ANOVA) is performed to measure percentage contribution of each factor. The results show that the pulse current was most influencing parameter on the depth of penetration and % on time (23.28%) the next most influencing. Confirmation test was carried out to validate the results of Taguchi analysis; the result shows that there is good match between expected and predicted results.
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The robust parameter design is effective method that is to produce high quality product at low cost to the manufacturer. This paper analyses disadvantages of Taguchi quality method, of which the amount of calculation is increased, algorithm is complicated, and the optimal value of variables in parameter design is updated in regression analysis. Having discovered a lot of useful information not to be utilized in Taguchi quality method, this paper introduces a new robust design method based on orthogonal optimization and variance ratio analysis. The optimal values of parameter and tolerance, at one time, can be obtained by means of successive-orthogonal arrays and variance ratio analysis in parameter design. The best solution in last orthogonal arrays and its tolerance selected by variance ratio analysis make up of factors in successive-orthogonal arrays. The bigger is variance ratio in last orthogonal arrays, the smaller is forecast range of tolerance in successive-orthogonal arrays. It is not necessary to make tolerance design after parameter design and to use crossed array design, signal-to-noise ratio analysis and regression analysis. Numerical result shows that the new method is far simpler and more convenient than Taguchi quality method.
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The experiment is conducted in an auto feed lathe. The temperature is controlled by a thermocouple and automated flame heating system. The statistical analysis is done by Taguchi method. Taguchi designs provide a powerful and efficient method for designing products that operate consistently and optimally over a variety of conditions. The primary goal is to find factor settings that minimize response variation, while adjusting (or keeping) the process on target. A process designed with this goal will produce more consistent output. A product designed with this goal will deliver more consistent performance regardless of the environment in which it is used. Taguchi method advocates the use of orthogonal array designs to assign the factors chosen for the experiment. The most commonly used orthogonal array designs are L8, L16, L9 (i.e. eight experimental trials), L16 and L18. The power of the Taguchi method is that it integrates statistical methods into the engineering process. The significance of the control factors are found in the following order. Cutting speed – 19.55 m/min, Depth of Cut - 0.5 mm, Temperature - 600 degree, Feed - 0.05 mm/rev. From statistical design of experiments by Taguchi method (MINITAB software) and Hot Machining we find that the power required is decreased and the tool life is increased by 14.8 %.
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Taguchi's catalog of orthogonal arrays is based on the mathematical theory of factorial designs and difference sets developed by R. C. Bose and his associates.These arrays evolved as extensions of factorial designs and latin squares.This paper (1) describes the structure and constructions of Taguchi's orthogonal arrays, (2) illustrates their fractional factorial nature, and (3) points out that Taguchi's catalog can be expanded to include orthogonal arrays developed since 1960.
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Quality engineering is associated with Taguchi-class experimental design methods, which are based on elegant and sophisticated orthogonal arrays and the associated analysis of variance. Some mixture-type orthogonal arrays like the L12(211), L18(2137), and L36(211312) have been widely used since the 1950s, while other arrays such as the L8(27), L16(215), L9(34), L27(313), and L32(231) have been applied in industry since 1985. Currently, the 18 run L18(2137) and the 16 run L16(215) arrays are the most popular experimental design matrices of all Taguchi-class orthogonal arrays. In Dr. Genichi Taguchi's experimental design textbook (Design of Experiments, 3rd Edition, 1976; published by the Maruzen Publication Co.), 16 out of all 40 case studies were based on the L16(215) array, nine were based on the L8(27) array, seven on the L27(313) array, five on the L9(34) array, one case study was based on the L32(231), and none were based on either the L12(211) or the L18(2137).
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The Taguchi parameter design in industry is an approach to reducing performance variation of quality characteristic in products and processes. In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. It often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was studied. In these studies, only single response was considered. In this paper we propose how to simultaneously optimize multiresponse for the robust design using the combined array approach.
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Design of Experiment (DOE) is a widely used method for examining experiments especially in industrial production and robust design processes. This method is a set of statistical approaches in which mathematical models are developed through experimental testing to estimate possible outputs and given input values or parameters. The method aims to determine the main factors that affect the results with the smallest number of experimental studies. In this study, L16 (2^15) orthogonal array, which was used in the Taguchi parameter design was reconstructed with the Support Vector Machines learning model and the Pearson VII kernel function. With this model, array elements were successfully classified in 87.04%. The new and original array were compared and 3.8% difference was measured between their Signal to Noise (S / N) ratios in an exemplary experiment.
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