Optimal Selection Of Machining Parameters In CNC Turning Process Of EN-31 Using Intelligent Hybrid Decision Making Tools

Nowadays numerical and Artificial Neural Networks (ANN) methods are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Therefore the present research is aimed at finding the optimal process parameters for turning process of EN-31. EN-31 is chosen as the work material because of its wide applicability as material for Ball and roller bearings, spinning tools, Beading rolls, Punches and dies and by its character it has very high resistance nature against wear and can be used for components subjected to severe abrasion, wear or high surface loading. Turning is used to produce rotational, typically axi-symmetric, parts that have many features, such as holes, grooves, threads, tapers, various diameter steps, and even contoured surfaces. Turning is also commonly used as a secondary process to add or refine features on parts that were manufactured using a different process. Due to the high tolerances and surface finishes that turning can offer, it is ideal for adding precision rotational features to a part whose basic shape has already been formed. Keeping in view of the importance of turning process, it is very important to automate the process. In order to automate the system or process it is essential to find the optimal process parameters. Hence intelligent hybrid decision making tools are applied to find the optimal process parameters. First the experiments were conducted as per the design of experiments, and then ANN is applied to predict the models for the chosen output responses. Later the models after testing for its adequacy using ANOVA Analysis, they may be chosen for subsequent optimization of the process parameters using Evolutionary techniques. The obtained optimal process parameters will be used to automate the process (C) 2014 Published by Elsevier Ltd.