Session: CIE-13-01 - SEIKEM: Design Informatics

Paper Number: 90465

90465 - A Data-Driven Design Approach for Carbon Emission Prediction of Machining 

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. However, due to the complexity of machining, the carbon emissions are influenced by many impact factors, and show dynamic relationship with them. At the moment, these indicators and relationship are difficult to be fully considered in the existing method, which may cause the inaccurate results of carbon emission prediction. 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.

Presenting Author: Yuxuan Chen Wuhan University of Science and Technology

Presenting Author Biography: Yuxuan Chen, male, was born in 1997. He got his bachelor's degree from Wuhan University of Arts and Science in 2019, and he is now pursuing his master's degree at Wuhan University of Science and Technology.

Authors:

Yuxuan Chen Wuhan University of Science and Technology
WEI YAN Wuhan University of Science and Technology; Cardiff University
Hua Zhang Wuhan University of Science and Technology
Ying Liu Cardiff University
Zhigang Jiang Wuhan University of Science and Technology
Xumei Zhang Wuhan University of Science and Technology