Statistical Analysis of Tool Wear Using RSM and ANN
The research reported herein is to model the tool wear during face milling of Hybrid composites using response surface methodologies (RSM) and artificial neural network (ANN). Aiming to achieve this goal, several milling experiments were carried out with
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Abstract The research reported herein is to model the tool wear during face milling of Hybrid composites using response surface methodologies (RSM) and artificial neural network (ANN). Aiming to achieve this goal, several milling experiments were carried out with polycrystalline diamond (PCD) inserts at different machining parameters namely cutting speed, feed, depth of cut, and weight fraction of Al2O3. Materials used for the present investigation are Al 6061-aluminum alloy reinforced with alumina (Al2O3) of size 45 microns and graphite (Gr) of an average size 60 μ, which are produced by stir casting route. Central composite face centered second order RSM was employed to create a mathematical model and the adequacy of the model was verified using analysis of variance. Comparison has been made between prediction capabilities of model based on RSM and ANN. The comparison clearly indicates that the models provide accurate prediction of tool wear in which ANN perform better than RSM. Keywords Hybrid composites • Milling • PCD insert • RSM • ANN
1 Introduction Composites are synthetically made novel class of materials consisting of distinct insoluble phases and deriving the advantage of each phase to enhance the overall performance of the material. Hybrid Metal Matrix Composites are new class
A. Arun Premnath (*) Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Enathur, Kancheepuram, Tamil Nadu 631561, India e-mail: [email protected] T. Alwarsamy Directorate of Technical Education, Chennai, India e-mail: [email protected] T. Abhinav Indian Institute of Technology, Chennai, India e-mail: [email protected]
S. Sathiyamoorthy et al. (eds.), Emerging Trends in Science, Engineering and Technology, Lecture Notes in Mechanical Engineering, DOI: 10.1007/978-81-322-1007-8_27, © Springer India 2012
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of composites, which has better mechanical and machining properties than conventional composites. Metal matrix composites (MMCs) combine metallic properties with ceramic properties, which increase the strength of these materials. The increase in strength finds them difficult to machine due to their extreme abrasive properties [1]. Also, they lead to improper surface finish and wear of tools. In order to overcome the above said difficulties during machining, a small amount of softer reinforcement is added to composites, which will favors machining process. The composites materials thus obtained, with more than one reinforcement are termed as hybrid composites. Many researchers have studied the effect of machining parameters in the various machining process on tool wear. Rajesh Kumar Bhushan et al. [2]. has investigated the effect of cutting speed, depth of cut, and feed rate on surface roughness and tool wear rate during the machining of 7075 Al alloy and 10 % wt. SiC particulate metal-matrix composites; tungsten carbide and polycrystalline diamond (PCD) inserts have been used as cutting tools. Munoz-Escalona and Maropoulos [3] studied th
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