Abstract
Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.
Highlights
Metal cutting is one of the most commonly utilized manufacturing processes, the improvement in the performance of these processes and productivity based on cutting parameters like cutting tool geometry, cutting conditions in addition to the tool material and workpiece is essential
The experiment plan is designed to determine the impact of feed rate, cutting speed, and cutting depth on surface roughness roughness values (Ra)
The machining zone accumulates less heat. It is observed in figure 5 that as the feed rate increase from 0.07 mm/rev to 0. 3 mm/rev, the surface roughness steadily rising, due to the higher friction between cutting tool and work material as a result of greater cross-sectional area in the deformation region [19]. It is noted in the main impact plot in Fig. 5 that the surface roughness decreases as the cutting depth increases from 0.2 mm to 0.6 mm which is in agreement with Prasad et al [19]
Summary
Metal cutting is one of the most commonly utilized manufacturing processes, the improvement in the performance of these processes and productivity based on cutting parameters like cutting tool geometry, cutting conditions in addition to the tool material and workpiece is essential. The surface feature is an essential parameter for determining the performance of machine tools and parts. Achieving the required surface quality is of great importance for the practical behavior of mechanical parts. The surface roughness is a widely utilized indicator of product quality in terms of various parameters such as fatigue life improvement, tribological considerations, corrosion resistance, etc. Variety of factors like cutting fluid, cutting parameters, and the hardness of the workpiece have been found to affect surface roughness in the turning process in different amounts [2]
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