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An Investigation of Energy Efficiency in Finish Turning of EN 353 Alloy Steel

机译:EN 353合金钢终止转型能效研究

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Mechanical machining is a significant part of manufacturing industries, which usually includes the cutting of metals to remove unwanted material using different cutting tools. The EN 353 alloy steel is widely utilized to manufacture critical parts of an automobile such as heavy-duty gear, camshaft, gudgeon pins, shaft, and pinion etc. which require significant strength, hardness, resilience and surface quality. Thus, it needs to be investigated from the point of view of energy efficiency (EE) during its processing to finish parts. The high EE of the machining processes is generally the key factor for reducing the electricity bills and carbon emissions. In the present work, the EE is optimized with the Taguchi method and modelled with response surface methodology (RSM) for the finish turning of EN 353 alloy steel. The interactions between cutting variables and nose radius were considered in experimental design, and the cermet inserts were used. The results reveal that at the optimum finish turning variables, there is a 78.92% enhancement in EE as compared to finish tuming settings utilized in industries. The optimum value of EE is achieved by cutting variables at higher levels and nose radius at a middle level. The analysis of variance (ANOVA) results indicates that the feed rate has minimal influence on the response and the depth of cut has an immense effect followed by cutting speed and nose radius. The coefficient of determination 93.02% for the regression model is relatively high, which indicates the model's abilities to create correct predictions. Further, the optimization energy parameters can be considered along with other quality features for more realistic results.
机译:机械加工是制造业的重要组成部分,通常包括切割金属,以使用不同的切削工具去除不需要的材料。 EN 353合金钢广泛用于制造汽车的关键部件,如重型齿轮,凸轮轴,欺骗销,轴和小齿轮等,这需要显着的强度,硬度,弹性和表面质量。因此,需要在其处理期间从能效(EE)的角度来研究它以完成零件。加工过程的高EE通常是减少电费和碳排放的关键因素。在本作工作中,EE用Taguchi方法进行了优化,并用响应表面方法(RSM)进行了建模,用于EN 353合金钢的终止转动。在实验设计中考虑了切割变量和鼻径半径之间的相互作用,使用了金属陶瓷插入物。结果表明,在最佳完成转变变量下,与在行业中使用的流动设置结束时,EE的增强率为78.92%。通过在中间水平下切割变量和鼻径半径的变量来实现EE的最佳值。方差分析(ANOVA)结果表明,进料速率对响应的影响最小,切割深度具有巨大的效果,然后切割速度和鼻径。回归模型的确定系数93.02%相对较高,这表明模型创建正确预测的能力。此外,可以考虑优化能量参数以及其他质量特征,以实现更现实的结果。

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