Investigation of Surface Roughness in Machine using Artificial Intelligence Techniques

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI:10.1109/IC3IOT53935.2022.9767952

C. Mahesha, R. Suprabha, G. Puthilibai, V. Devatarika, J. R, D. R

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引用次数: 5

Abstract

Carbon fiber and carbon fiber composites have become more widely used in a multitude of sectors, including defensive line, military, as well as industries. Surface quality is also given careful consideration, as machineries rely on matching parts to work. The Carbon Fiber Reinforced Polymer (CFRP) turning process composites is explored in this research by adjusting three critical cutting variables: cutting speed, depth of cut and feed. Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) have been used to develop an experimental model for assessing surface roughness. The three cutting variables were assessed as experimental design input criteria for these models. In the context of ANN methodology, the traditional backpropagation technique was found as the best option for training the model. Analysis of variance which is referred as ANOVA was used to determine the consequence of cutting parameters on roughness of respective surface. $R^{2}$, RMSE and MEP were computed as 99.9%, 0.016 and 2.17 respectively based on RSM modelling results

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基于人工智能技术的机械表面粗糙度研究

碳纤维和碳纤维复合材料已经越来越广泛地应用于许多领域，包括防线，军事以及工业。表面质量也要仔细考虑，因为机械依赖于匹配的零件来工作。通过调整切削速度、切削深度和进给量这三个关键切削变量，对碳纤维增强聚合物车削复合材料进行了研究。利用人工神经网络(ANN)和响应面法(RSM)建立了表面粗糙度评估的实验模型。这三个切削变量被评估为这些模型的实验设计输入标准。在人工神经网络方法的背景下，发现传统的反向传播技术是训练模型的最佳选择。方差分析被称为方差分析，用于确定切削参数对各自表面粗糙度的影响。$R^{2}$，根据RSM建模结果计算RMSE和MEP分别为99.9%，0.016和2.17

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2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)

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