An Improved Prediction Model for Drilling Characteristics of Al/SiC Metal Matrix Composites

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Aluminium Silicon Metal Matrix Composites (ASMMCs) are new generation engineering materials that possess improved physical and mechanical and thermal properties. The drilling of Al/SiC metal matrix composite has received a serious attention for many years. The drilling is a process of high complexity due to its special difficulties such as cutting in a closed and limited space, high cutting temperature and the difficulty of chip formation and removal. These types of operations, known as cutting speed, cutting environment, point angle, feed rate and devices. Surface finish has been an important factor of machining in predicting performance of any machining operation. A lot of researches have focused on modelling and prediction of surface roughness of Al/Sic MMCs, however, the modelling fails when the input models and output categorization varies. In this paper, we propose an improved prediction technique, which is from the inspiration of artificial neural network, to learn the drilling performance of MMCs under various inputs and output characteristics. This paper is considers two Al/SiC and Al/SiC/B4C MMCs and their different test data on drilling performance. The experimental results prove that the error gets minimized in the improved prediction model when compared to earlier neural models.
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Al/ Si; MMC; Prediction Model; Particle Swarm Optimization (PSO); Fine Tuning

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