Manufacturing Technology Today

K. L. Senthil Kumar ¹, R. Sivasubramanian ²

Affiliations
1 Dept. of Mechanical Engg., Bannari Amman Institute of Technology, Sathyamangalam-638401, Erode District, IN
2 Dept. of Mechanical Engg., Coimbatore Institute of Technology, Coimbatore-641014, IN

Abstract

In the present study A356/SiC_p metal matrix composite is machined using electro chemical machining. Silicon Carbide with an average particle size 0/40 microns is tried in three different proportions, namely 5%, 10% and 15% by weight. Taguchi's L₂₇ orthogonal array is chosen to design the experiments and two repetitions for every combination are made to study the effect of various parameters chosen. Material removal rate is determined for different combinations of applied voltage, electrolyte concentration, feed rate and percentage of silicon carbide. Optimum factors are identified from the signal-to-noise ratio. Result from Pareto ANOVA, which is a method to analyze data, showed that feed rate is the most influencing parameter followed by electrolyte concentration in maximizing the material removal rate. A regression model is also developed and found that the experimental data closely agree with the prediction.

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M. Jayaraman ¹, R. Sivasubramanian ², V. Balasubramanian ³, A. K. Lakshminarayanan ³

Affiliations
1 Dept. of Mechatronics Engg., Kongu Engg. College, Perundurai, Erode, IN
2 Dept. of Mechanical Engg., Coimbatore Institute of Technology, Coimbatore, IN
3 Centre for Materials Joining Research (CEMAJOR), Dept. of Manufacturing Engg., Annamalai University, Annamalai Nagar, IN

Abstract

This paper discusses the use of Taguchi technique for maximizing the tensile strength of friction stir welded cast aluminium alloy A413. The experiments have been conducted using Taguchi's experimental design technique. The friction stir welding (FSW) process parameters namely tool rotational speed, welding speed, axial force play a major role in deciding the weld quality. The effect of process parameters on tensile strength is evaluated and the optimum welding condition for maximizing the tensile strength is determined. The analysis of variance and the signal to noise ratio of robust design are employed to investigate the influence of process parameters on the tensile strength of friction stir welded A413 aluminium alloy. To correlate the process parameters and the measured tensile strength, a mathematical model has been developed by multiple linear regression analysis. The developed mathematical model is found to be very useful for predicting the tensile strength of friction stir welded A413 aluminium alloy.

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N. Mohanasundararaju ¹, R. Sivasubramanian ²

Affiliations
1 Department of Mechanical Engg., Sona College of Technology, Salem-636005, IN
2 Department of Mechanical Engg., Coimbatore Institute of Technology, Coimbatore, IN

Abstract

In industrial manufacturing, grinding processes are used for finishing of workpieces. The work rolls used in Sendzimir cold roll mills were ground in the roll grinding shop to remove the marks formed in the surface of the work rolls during rolling process. Since Sendzimir mills were driven by contact friction, the work roll should have suitable roughness for thickness reduction. To obtain the average roughness in work rolls consistently, the process has to be optimized and the optimal level of the factors has to be determined. This paper deals with optimization of grinding parameters to obtain desired surface roughness in the work rolls using Neural Network-Taguchi approach. In this study, there are six factors wheel speed, workspeed, traverse speed, infeed, dress depth and dressing lead were considered to obtain a optimum grinding condition using Taguchi Techniques with L27 orthogonal array. An attempt was made to minimize the number of experimental runs and increase the reliability of experimental results. Artificial neural network (ANN) model was developed to enhance the prediction accuracy. The confirmation test was conducted to verify the results obtained from Taguchi Technique and Neural network model. Analysis of Variance (ANOVA) was conducted to identify the significant parameters during grinding process.

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