Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example

Abstract

It is an inevitable trend of enterprise development to optimize the low-carbon machining process and reduce the carbon emissions generated by this system. The traditional quality-based manufacturing method is no longer suitable for today’s concept of sustainable development. Therefore, a data-driven method based on uncertainty evaluation for low-carbon control in machining processes is proposed. Firstly, the framework for the data-driven method was established, then the data collection for the input and output in the machining process was carried out. Secondly, by establishing the carbon emission data model and analyzing data with carbon emission uncertainty evaluation indicators during processing, the carbon emission optimization strategy was proposed. Finally, axle processing technology was applied to the experimental verification, exploring the uncertainty of emissions finishing machining steps and other work sequences, while carrying out targeted strategy optimization, which verifies the feasibility and effectiveness of the method. The results show that the uncertainty of each process is reduced after optimization. This study provides theoretical and methodological support for promoting low-carbon emissions for manufacturing enterprises.