Analysis Of Machining

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2.1. Theoretical framework In a metal cutting operation, a cutting tool deforms the workpiece material until it shears off in the form of chips. The deformation process requires significant energy, and the tool endures a variety of mechanical, thermal, chemical, and tribological loads. These loads eventually cause the tool to deteriorate and wear out or fail. Therefore, the goal for having a good metal cutting application is to balance the energy required to remove metal with the tool’s ability to reliably withstand the load placed on it. Understanding and manipulating correctly cutting parameters, tool geometries, tool materials, and other factors enable machinists to achieve a productive and cost-effective metal cutting process. Mechanical …show more content…

The optimization of cutting parameters during machining is a difficult task as it involves a number of aspects such as knowledge of machining, empirical equations of tool life, cutting forces, power consumed, machining surface finish etc. All these aspects should be considered during machining optimization to develop an effective optimization criterion (Sonmez et el, 1999). Manufacturing industries have long depended on the skill and experience of shop-floor machine-tool operators for optimal selection of cutting conditions and cutting tools. Many authors as early as in the 19th century like Taylor, 1907 have shown the optimization objective as specific cost from the beginning of the researches in this branch when the area of the art of cutting metals was looked on. This was also considered by to some of the most recent work (Liang et el, 2001; wang et el, 2002; Saravanan, 2003 and Cus and Balic, …show more content…

In reality, no matter how experienced he may be or how skilled he maybe, it is still very challenging for him to apply the right input parameters to always attain an optimal output result. The key machining parameters in metal turning operations are its cutting speed, feed rate and depth of cut etc. these needs to be applied optimally before the result output will be the best. As discussed by Palanikumar, et al, the setting of these parameters determines the quality characteristics of the final turned parts. He applied the application of Taguchi method with fuzzy logic to optimize the machining parameters for machining of GFRP (Glass Fiber Reinforced Plastic) composites with multiple characteristics (Palanikumar, et al, 2006). The optimization process was implemented using the multi response performance index (MRPI) as the performance was tailored on the degree of the metal removal rate, surface roughness and tool wear with machine parameters like the work piece’s fibre orientation, feed rate, depth of cut, cutting speed and machining time. Srikanth and kamala (2008) both developed a real coded Genetic Algorithm (RCGA) approach for optimizing the cutting parameters in their turning process. This RCGA approach was quite beneficial in order to attain the minimum surface roughness values and their corresponding optimum cutting parameters, for certain

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