A Data-Driven Design Approach for Carbon Emission Prediction of Machining

Yuxuan Chen, W. Yan, Hua Zhang, Y. Liu, Zhigang Jiang, Xumei Zhang
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Abstract

The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple sources and impact factors of carbon emissions in machining are studied, and the relationship between these factors is also studied to describe the carbon emissions. Then, a data-driven approach is designed to predict the carbon emission of machining, which consists of data collection and preprocessing, feature extraction, prediction model establishment and model validation. The ridge regression, BP neural network based on Genetic Algorithm (GA-BP), root means square error (RMSE) and mean relative percentage error (MPAE) are respectively employed to fulfill the above tasks in the design approach. Finally, an experimental study of a real turning machining is proposed to verify the feasibility and merits of the designed approach.
加工碳排放预测的数据驱动设计方法
制造业的碳减排问题越来越受到关注。机械加工作为制造业的重要组成部分,通过资源消耗、能源消耗和废物处理产生了大量的碳排放。加工碳排放预测是降低加工碳排放的先验技术,已被确立为最重要的研究目标之一。本研究的目的是通过数据驱动的方法设计一个加工碳排放预测模型。首先,研究了加工中碳排放的多种来源和影响因素,并研究了这些因素之间的关系,以描述碳排放。然后,设计了一种数据驱动的加工碳排放预测方法,包括数据采集与预处理、特征提取、预测模型建立和模型验证。在设计方法中,分别采用脊回归、基于遗传算法的BP神经网络(GA-BP)、均方根误差(RMSE)和平均相对百分比误差(MPAE)来完成上述任务。最后,通过实际车削加工的实验研究,验证了所设计方法的可行性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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