Informatics Publishing Limited

S. Ananth ¹, J. Udaya Prakash ², T. V. Moorthy ³, P. Hariharan ³, R. Magendran ⁴, A. R. Sivanesh ⁴

Affiliations
1 Dept. of Industrial Engg., Jawahar Engineering College, Chennai, IN
2 Dept. of Mech. Engg., Vel Tech Dr.RR & Dr.SR Technical University, Chennai, IN
3 Dept. of Manufacturing Engg., Anna University, Chennai, IN
4 Dept. of Manufacturing Engg., CSI College of Engineering, Ketti, Nilgiris, IN

Abstract

This paper details the investigation of wear characteristics of gray cast iron under dry, SAE15W40 and SAE20W40 oil lubricated sliding conditions. The effects of applied load, sliding speed and sliding distance on the specific wear rate of gray cast iron were studied using pin-on-disc wear testing machine. The experimental data were analyzed by using the robust technique of Taguchi’s orthogonal arrays. The specific wear rate was analyzed using signal to noise ratio and analysis of variance.

Keywords

Gray Cast Iron, Specific Wear Rate, Taguchi Technique, Signal to Noise Ratio, Analysis of Variance.

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T. V. Moorthy ¹, N. Vinoth Babu

Affiliations
1 Dept. of Manufacturing Engg., CEG, Anna University, Chennai, IN

Abstract

Friction Welding (FRW) has been widely used for welding of similar and dissimilar metals. Structures made of Stainless steel cannot be entirely replaced with aluminium component. Presently the demand for FRW of such metals is increasing in many industrial fields. In this study the Direct Friction welding of low weldability Aluminium 6063 alloy and Type 304 Stainless steel was conducted and suitable welding conditions for producing a sound weld was investigated. In particular the effect on feature of weldment, its mechanical properties and microstructure were studied by varying the parameters and results were produced. Then the obtained experimental results were compared with that of the mathematical model developed by the Artificial Neural Network (ANN).

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T. V. Moorthy ¹, S. Ponnuvel ¹

Affiliations
1 Department of Manufacturing Engineering, Anna University, Chennai- 600 044, IN

Abstract

The aim of this paper is to structure ANN models which are capable of predicting the variations in the cutting forces, on drilling polycarbonate/glass fibre polymer composite material. The experimental observations were made at some selected cutting speed and feed combinations by drilling unified, 10%, and 20% glass filled polycarbonate, using HSS and carbide tipped drill bits of 6mm diameter. Appreciable amount of reduction in the cutting forces was found, when drilled with WC tipped drill bits. So developed, back propagation paradigm based ANN models with delta learning rule predicts, abovementioned variations with exceptional accuracy.

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