The automobile industry, as a representative in pursuing the goals of “emission peak” and “carbon neutrality”, has made low carbon a new industrial practice. With regard to low carbon, the lightweight design proves to be an effective approach to reducing carbon emissions from automobiles. Given the state of research, in which the existing lightweight design schemes of automobiles seldom consider the impact of the lightweight quality on carbon emissions during the whole life cycle of the automobiles, this paper proposes a more comprehensive lightweight design method for automobiles in regard to carbon emissions. First, the finite element method was adopted to analyze the stress, strain and safety factors of the automobile parts based on their stress, so as to identify the positions where the lightweight design was applicable. Subsequently, a lightweight scheme was designed accordingly. Next, the finite element method was re-applied to the parts whose weights had been reduced. In this way, the feasibility of the lightweight scheme was verified. In addition, a method of calculating the carbon emissions produced by changes in the mass, manufacturing processes, application and recycling of automobile parts after the application of the lightweight design was also presented. The method can be used for evaluating the low carbon benefits of the lightweight design scheme. To prove the feasibility of the method, the ZS061750-152101 wheel hub designed and manufactured by Anhui Axle Co., Ltd. was taken as an example for the case analysis. The lightweight design changes three structures of the wheel hub, reducing its weight by 1.4 kg in total. For a single wheel hub, the carbon emissions are reduced by 51.22 kg altogether. That is to say, if the lightweight scheme were to be applied to all the wheels produced by Anhui Axle Co., Ltd. (about 500,000 per year), the carbon emissions from the wheel production, application and recycling could be cut by 2.56 × 10⁷ kg, marking a favorable emission reduction effect. The proposed method can not only provide insight into the lightweight design of automobiles and other equipment against the background of low carbon but also provide a channel for calculating the carbon emission changes in the whole process after the application of the lightweight design.

As the working efficiency and life span of the shovel plate of a roadheader directly influence its performance, optimization of the shovel-plate parameters is crucial. For optimizing the shovel-plate parameters, the variations in the loading capacity and shovel grubbing force with respect to the shovel-plate parameters are determined in this study. Moreover, the ideal point method and gray weight method are proposed for multiobjective optimization. The gray weights of the loading capacity and shovel resistance are determined by investigating existing molded products and through the gray decision method. Thereafter, the particle swarm optimization (PSO) algorithm is applied for multiobjective optimization of the shovel-plate parameters. Considering the EBZ230-type roadheader shovel plate as an example, parameter optimization through multiobjective optimization decreases the mass of the shovel plate by 14.3% and the shovel resistance by 7.3%, while increasing the loading capacity by 1.4%. To demonstrate the influence of optimization, coal and rock excavation with a shovel is simulated using the optimized parameters in an ANSYS-Workbench environment. The results indicate that the maximum stress at the front of the shovel plate decreases by 22.1%, minimum fatigue life increases by 139.6%, and minimum safety factor increases by 30.3%. The obtained results establish that, in multiobjective optimization based on PSO, the ideal point method and gray weight method optimize the shovel-plate parameters. This optimization can provide a theoretical basis and reference values for the design of roadheader shovel plates and can be applied for multiobjective optimization in engineering as well.

Purpose: The uncertainty of remanufacturing parts is a key factor affecting the quality of remanufactured products. Therefore, the purpose of this paper is to measure the uncertainty of remanufactured parts and study the coupling mechanism of reassembly quality. Design/methodology/approach: First, uncertainty of remanufactured parts is analyzed, and the uncertainty measure model for remanufacturing parts based on entropy is constructed. Second, the nonlinear mapping model between the uncertainty and reassembly quality were studied using Gauss-Newton iterative method to reveal the coupling mechanism between uncertainty of remanufacturing parts and reassembly quality. Finally, the model is verified in the reassembly process of remanufacturing cylinder head. Findings: The method can guide reassembly operations to improve the reassembly quality with uncertainty of remanufactured parts. Originality/value: This study provides practical implications by developing a multivariate nonlinear mapping model for reassembly quality based on entropy to determine the uncertainty factors that affect the reassembly quality significantly and then correct the reassembly operation to better optimize the allocation of remanufacturing production resources. The study also theoretically contributes to reveal the coupling mechanism of reassembly quality with the uncertainty of remanufactured parts.

The lightweight design is considered to be of great significance for carbon reduction. Therefore, a lightweight design of a special axle hub is proposed based on ANSYS considering different working conditions. The structure of a special axle hub is identified. The ANSYS Workbench is used to mesh the hub and import the mechanical and physical parameters. Considering full load, emergency turning and braking, and the treatment of constraints and loads, the stress concentration position is obtained, and the lightweight design is carried out. The free vibration analysis and fatigue life prediction of the hub after lightweight design are verified. After the lightweight design, the optimized hub mass is reduced by 7.52, and its structural stability and strength meet the demands of various working conditions. The application of a lightweight hub reduces CO₂ emissions by 49875kg during the life cycle of the vehicle. This study provides theoretical and methodological support for lightweight design of mechanical parts for low carbon.

The development of tunneling equipment still lags behind, limiting rapid and accurate tunneling and restricting efficient production in coal mines. Thus, improving the reliability and design of roadheaders becomes essential. As the shovel plate is an essential part of a roadheader, improving its parameters can increase the roadheader performance. The parameter optimization of roadheader shovel plate is multi-objective optimization. Because of conventional multiobjective optimization requires strong prior knowledge, often provides low-quality results, and presents vulnerability to initialization and other shortcomings when used in practice. We propose an improved particle swarm optimization (PSO) algorithm that takes the minimum Euclidean distance from a base value as the evaluation criterion for global and individual extreme values. The improved algorithm enables multiobjective parallel optimization by providing a non-inferior solution set. Then, the optimal solution is searched in this set using grey decision to obtain the optimal solution. To validate the proposed method, the multiobjective optimization problem of the shovel-plate parameters is formulated for its solution. Before optimization shovel-plate most important parameters l is the width of the shovel plate [ ] = 3.2 m, [ ] is the inclination angle of the shovel plate and β = ,19°. When doing optimization, set accelerated factor [ ], population size N = 20, and maximum number of iterations [ ] = 100. In addition, speed V was restricted by [ ], and inertia factor W was dynamic and linearly decreasing, [ ], with [ ] and [ ]. In addition, [ ] and [ ] were set randomly in [0, 1], while optimization degree η was set to 30%. And then we executed the improved PSO, obtaining 2000 non-inferior solutions. Apply grey decision to find the optimal solution. The optimal roadheader shovel-plate parameters are l = 3.144 m and β = 16.88°. Comparative analysis is made before and after optimization, the optimized parameters were substituted into the model and simulated. Obtained that the optimized parameters of shovel-plate can reduce the mass of the shovel plate decreases by 14.3%, while the propulsive resistance decreases by 6.62%, and the load capacity increases by 3.68%. Thus jointly achieving the optimization goals of reducing the propulsive resistance while increasing the load capacity. The feasibility of the proposed multiobjective optimization method with improved particle swarm optimization and grey decision is verified, and the method can provide convenient multiobjective optimization in engineering practice.