Mathematical modelling of carbon emissions and process parameters optimisation for laser welding cell

Abstract

Laser welding has been widely employed for aluminum alloy body-in-white. However, owing to the low utilisation efficiency of energy and material, the carbon emission of laser welding is serious. In this paper, the mathematical modelling of carbon emissions and process parameters optimisation for laser welding cells are studied. The carbon emission characteristics of the laser welding system are analysed, and the carbon emission model of the laser welding cell considering both welding features and welding transfers is established. Besides, the process parameters optimisation model of laser welding cell is developed. To obtain optimal welding sequence, a combinatorial algorithm based on Culture Algorithm and Ant Colony Algorithm is proposed. Based on the optimal welding sequence, the optimal transfer speed is obtained through mathematical theory. Furthermore, a case study is performed to verify the feasibility and reliability of the process parameters optimisation model. Additionally, compared with the Ant Colony Algorithm, the proposed solution algorithm has better comprehensive performance in terms of convergence speed and optimisation accuracy. This study lays a theoretical foundation for carbon emission modelling of the laser welding cell, and can support the automobile enterprise to make processing parameters selection of the laser welding line in the design stage.

KEYWORDS:

• Laser welding cell
• carbon emission
• culture-ant colony algorithm
• welding sequence
• transfer speed

Acknowledgements

The author would like to thank the anonymous reviewers and editors, whose valuable comments and corrections will improve this work substantially.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Project of International Cooperation and Exchanges NSFC [grant number 51861165202], National Natural Science Foundation of China NSFC [grant number 51805066], and Graduate Research and Innovation Foundation of Chongqing [grant number CYB21008].

Notes on contributors

Weiwei Ge

Weiwei Ge is currently pursuing his Ph.D. degree at College of Mechanical and Vehicle Engineering, Chongqing University. His research interests include laser welding system, manufacturing system engineering, and production planning and scheduling. His work has been published in the International Journal of Advanced Manufacturing Technology.

Hongcheng Li

Hongcheng Li is an associate professor of Chongqing University of Posts and Telecommunication. His current research focuses on intelligent manufacturing system, low carbon manufacturing process and equipment, and industrial big-data. His work has been published in several international conferences and journals such as International Journal of Precision Engineering and Manufacturing-Green Technology, Energy, International Journal of Production Research, Journal of Cleaner Production, and International Journal of Computer Integrated Manufacturing.

Xuanhao Wen

Xuanhao Wen is currently pursuing his Ph.D. degree at College of Mechanical and Vehicle Engineering, Chongqing University. His research interests include manufacturing system engineering, energy efficiency management and optimisation. His work has been published in Energy.

Chengchao Li

Chengchao Li is currently pursuing his master degree at College of Mechanical and Vehicle Engineering, Chongqing University. His research interests include manufacturing system engineering, energy efficiency management, and industrial big-data.

Huajun Cao

Huajun Cao is a professor, director of Institute of Manufacturing Engineering, Chongqing University, the affiliate member of CIRP, and he was granted as the young scholar of ‘Changjiang Scholars Program’ and the scientific and technological innovation leading talent of ‘Ten-thousand People Program’ in 2015 and 2018, respectively. His research interests mainly include green manufacturing and remanufacturing, manufacturing system engineering, and high-speed dry machining technology and equipment. His work has been published in several international conferences and journals, including the following: Energy, Robotics and Computer-integrated Manufacturing, IEEE Transactions on Automation Science and Engineering, Resources Conservation and Recycling, International Journal of Precision Engineering and Manufacturing-Green Technology.

Bin Xing

Bin Xing is a professor-level senior engineer of Chongqing Industrial Big Data Innovation Center. His research interests include intelligent manufacturing, internet of things, and industrial big-data.