Experimental Study, Neural Network Modeling and Optimization of Environment-Friendly Air-Cooled and Dry Turning Processes

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Manufacturing processes are studied scientifically for improving the quality and productivity. Lately, there has been an increasing concern about the consideration of environment in manufacturing [Sheng and Srinivasan, 1995; Gungor and Gupta, 1999; Yan et al., 2007], which is not only related with the protection of human and the surrounding environment, but is also related with the reduction of the resource consumption in manufacturing processes. Green manufacturing and green engineering in general have become popular in industrial processes and products. According to the US Environmental Protection Agency, green engineering is the design, commercialization and use of processes and products, which are feasible and economical while minimizing (a) generation of pollution at the source and (b) risk to human health and the environment. Machining is one of the important and widely investigated manufacturing processes. Nevertheless, the practical implementation of research findings is very difficult due to a variety of factors and the statistical nature of the machining process. For example, the maximum performance in turning can be achieved by selecting the proper process parameters and coolant. However, the cutting behavior is different for each tool-workpiece material combination, besides being dependent on several other factors such as the rigidity of the machine tool. This necessitates conducting of a number of experiments for each tool-work material combination. The rigorous experimental work under various cutting conditions and at different cutting environment can help to take a conclusive decision in achieving the desired performance of the machining. The focus of the present thesis is on exploring the environmental-friendly options for the turning process. Hence, dry turning and aircooled turning processes have been studied. The dry and air-cooled turning processes do not use a cutting fluid, thus avoiding the pollution at the shop floor and...
Supervisor: U. S. Dixit