Applied Mechanics and Materials Vols. 592-594

Abstract: Genetic algorithm has been proven as one of the most popular optimization techniques for the parametric optimization of conventional machining processes. In this study genetic algorithm has used to optimize the process parameters. Aim of the present study was to develop empirical model for predicting surface roughness in terms of spindle speed, feed rate and depth of cut using multiple regressions modelling method. Experiments were carried out on NC controlled machine tool by taking AlMg1SiCu as workpiece material and carbide inserted cutting tool. Finally, genetic algorithm has been employed to find out the optimal setting of process parameters that optimize surface roughness. This provides flexibility to the manufacturing industries to choose the best setting depending on applications.

Abstract: Ultrasonic welding (USW) is a solid-state joining process in which the joint is created between the workpieces by the application of high frequency ultrasonic waves under pressure. Poor weld strength is one of the major problem experienced in the application of such weldments. It is mainly due to improper selection of process parameters. In order to achieve satisfactory weld strength optimization of welding process parameters is indeed essential. In this present context, the USW has been carried out to join dissimilar materials like A1100 aluminum alloy and CuZn37 brass plates of thickness 0.1 mm using Taguchi’s L₁₆ orthogonal array design of experiments. The process parameters like amplitude, weld pressure and weld time have been taken into consideration. Taguchi’s S/N ratio concept has been employed to study the effect of different process parameters on the response like tensile strength; and finally, the optimum setting of process parameters has been decided in view of maximizing tensile strength. The predicted result of the optimized tensile strength has been validated by conducting a confirmatory test. This analysis shows that the high quality and efficient joints can be generated by exploring optimized setting of process parameters which is fruitful in mass production as well as off-line quality control of such a welding practice.

Abstract: This paper presents an optimum method to find the significant parameters affecting Wire Electrical Discharge machining (WEDM) performance using Grey relational analysis. A413 Aluminium Alloy reinforced with 20 microns of Boron Carbide and 75 microns of Fly Ash, hybrid composites was fabricated using stir casting technique. Experiments have been conducted with the process parameters like pulse on time, pulse off time, wire feed, gap voltage and weight percentage reinforcement with three different levels. The influence of each parameter on the responses material removal rate and surface roughness is established using analysis of variances (ANOVA). The optimal machining-parameters setting for minimum surface roughness and maximum material removal rate was obtained by applying Grey relational analysis.

Abstract: Recent evidences shows that the more and more manufacturing companies are recognizing the importance and necessity of quality improvement if they are to survive domestic and world-wide competition..Day by day technology is changing, accordingly the approach and the techniques to manage overall processes are also renewed. A systematic and efficient way to meet this challenge is the continuous process improvement. The continuous process improvement can be achieved by practicing TQM concepts. The important concepts are optimization of performance, quality and cost.. In this paper an attempt is made in an industry to optimize the process parameters of the welding process so that the overall performance of the process and the quality of product is improved. The Taguchi technique uses a tool called Orthogonal Array and a measure of quality called Signal-to-Noise ratio. .ANOVA is used to optimize the welding parameters (input parameters) to find the responses {output parameters). The optimized input parameters are used to compare the output responses for both theoretical and actual, those obtained from the profile projector. The actual values are found to be comparable to the calculated values.

Abstract: The objective of the paper is to obtain an optimal setting of turning process parameters (cutting speed, depth of cut and feed rate) resulting in an optimal value of the feed force when machining En19 steel with tungsten carbide cutting tool inserts. The effects of the selected turning process parameters on feed force and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s parameter design approach. It was indicated by the results that the selected turning process parameters significantly affect the selected machining characteristic. The percent contributions of parameters as quantified in the S/N ANOVA envisage that the relative power of cutting speed (72.09 %) in controlling variation and mean feed force is significantly higher than that of the depth of cut (22.30 %) and feed rate (05.31 %). The predicted optimum feed force is 98.067 N. The results have been validated by the confirmation experiments.

Abstract: The gas tungsten arc welding (GTAW) process is generally implemented for fusion welding of stainless steel, magnesium alloys, nickel base alloys, carbon steel and low alloy steels. This study deals with the parametric modeling of gas tungsten arc (GTA) welding process for two dissimilar metals specifically stainless steel and low carbon steel. There are several process parameters influences the quality of weld strength namely arc voltage, gas flow rate, torch distance, current and welding speed to be used. Among the various process parameters, the arc voltage, gas flow rate and torch distance were considered for this analysis with the response of tensile strength. Twenty experiments were performed as per response surface methodology (RSM) based central composite face centered design for GTAW process. Comparison studies were made for predicted and experimental values of tensile or weld strength by using analysis of variance (ANOVA). It was found that developed model statistically fit on 95% confidence level.

Abstract: The present study deals with the Taguchi technique applied to determine the optimal process parameters of Boron Carbide (B₄C) powder mixed electrical discharge machining (EDM) of Titanium alloy. The performance characteristics like material removal rate (MRR) and surface roughness (SR) were experimentally explored for various input parameters such as discharge current, pulse on time, pulse off time and B₄C powder concentration in dielectric fluid. The planned optimal setting parameters were conducted and verified through experiments and analyzed using Taguchi technique. Analysis of variance (ANOVA) revealed that discharge current, pulse on time and B₄C powder concentration in dielectric are most important parameters affecting both the performance parameters.

Abstract: Electric discharge machining process is an unconventional machining process primarily used for machining the materials such as difficult to machine in conventional machining process, hardest material and composite materials. In the present work, a study is made to find out the optimum EDM process parameters during machining of AA6061-15% boron carbide composite fabricated through stir casting technique. Three process parameters such as Current, pulse on time and pulse of time are opted as machining parameter variables. Response surface methodology is used to formulate the mathematical model for material removal rate, tool wear rate and surface roughness. Response surface methodology and genetic algorithm are applied to optimize the machining parameters individually by taking combined objective function and compared. Genetic algorithm optimization techniques yields better results than desirability approach. Key words: Electric discharge machining, MRR, TWR, Ra, RSM, Genetic algorithm

Abstract: Prediction of life of die block is an important activity of die design usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of die block of compound die using artificial neural network (ANN) is presented. The parameters affecting life of die block are investigated through Finite Element Method (FEM) analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different die block. A sample run of the proposed ANN model is also demonstrated in this paper.

Abstract: Now a day’s researchers are focusing on natural fiber composites. In the present work composites were prepared with epoxy (Araldite LY-556) resin and ‘emu’ bird feathers as fiber. The composites were prepared by varying the weight percentage (P) of ‘emu’ fiber ranging from 1 to 5 and length (L) of feather fibers from 1 to 5 cm. The various mechanical properties like tensile strength, flexural strength; flexural modulus and impact strength were determined. An attempt is made to model the mechanical properties through response surface methodology (RSM). Analysis of Variance (ANOVA) is used to check the validity of the model. The results reveal that the developed models are suitable for prediction of mechanical properties of Epoxy ‘Emu’ Feather Fiber Composites.