Multi-objective optimization model for mechanical processing based on improved PSO algorithm and carbon emission quantification algorithm

Abstract

Abstract As a high energy consuming and high emission industry, the carbon emissions of the manufacturing industry have attracted much attention. Traditional carbon emission methods often struggle to achieve real-time optimization and have limited applicability under complex process conditions. Therefore, this study proposes a multi-objective optimization method for mechanical processing based on a particle swarm optimization algorithm and a carbon emission quantification algorithm. This method first establishes a carbon emission model for the mechanical processing process, analyzes the carbon emission factors at different stages, and optimizes process parameters and routes through an improved particle swarm optimization algorithm. The results indicated that as the number of processed products increased, the carbon emissions corresponding to each model also increased. When the number of processed products reached around 500, the carbon emissions of each model tended to stabilize, with a carbon emission of 0.43 kg CO2. However, when the number reached around 400, the processing time was 0.52 h. Research has shown that the proposed optimization algorithm and carbon emission quantification algorithm for multi-objective optimization of mechanical processing can effectively reduce carbon emissions. This provides a scientific basis for the low-carbon transformation of the manufacturing industry.

Keywords

• Carbon emissions
• Multi-objective
• Machining
• PSO
• Low carbon