Awan Muhammad Rizwan, González Rojas Hernán A, Hameed Saqib, Riaz Fahid, Hamid Shahzaib, Hussain Abrar
Department of Mechanical Engineering, Universitat Politecnica De Catalunya (UPC), 08034 Barcelona, Spain.
Department of Mechanical Engineering, The Superior University, Lahore 54000, Pakistan.
Sensors (Basel). 2022 Sep 21;22(19):7152. doi: 10.3390/s22197152.
Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC-C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.
切断操作在制造业中广泛应用且能源密集度高。使用数据驱动模型预测单位能耗（SEC）是理解、分析和降低切断磨削能耗的一种很有前景的方法。本文旨在提出一种新颖的方法，使用监督机器学习技术预测并验证无氧铜（OFC-C10100）切断磨削的单位能耗。设计了先进的实验装置，以便在各种切削条件下对材料进行磨削切割。首先，利用高斯过程回归、回归树和人工神经网络（ANN）这三种监督学习技术，基于进给速度、切削厚度和刀具类型等输入工艺参数预测能耗值。在这三种算法中，发现高斯过程回归性能更优，在验证和测试期间误差最小。然后利用预测的能耗值评估单位能耗（SEC），结果显示其高度准确，相关系数为0.98。预测的单位能耗（SEC）与材料去除率的关系与物理模型中描述的关系吻合良好，这进一步验证了预测模型的准确性。
