Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

Abstract

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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

“…Following a comparison with other research [11,12,19], this method has the following advantages: By analyzing the uncertainty of carbon emissions of the enterprise's axle processing data, the current situation and difficulties faced by the enterprise in the current production can be accurately diagnosed. It can provide practical method guidance for the green production of enterprises.…”

“…In recent years, many experts and scholars have studied carbon emissions in manufacturing enterprises and found a series of important results in theory and practice: Gao et al [10] developed a new mathematical model to predict carbon emissions in the stamping process and achieve carbon reduction through process decomposition. Xiao et al [11] established a low-carbon and low-cost multi-objective optimization model according to the processing characteristics of complex box-like blank parts and used a particle swarm algorithm to solve the optimization model to meet the low-carbon demand. Jeswiet et al [12] proposed a quantitative model of carbon emissions for the manufacturing process.…”

Research on Assembly Sequence Optimization Classification Method of Remanufacturing Parts Based on Different Precision Levels

“…In the introduction, references [3][4][5][6][7][8][9][10][11] provide many ideas and methods for remanufacturing and assembly, effectively achieving the goal. In reference [4], an assembly sequence optimization model based on the lowest quality loss cost was established and solved by an ant colony algorithm.…”

“…Geda et al [7] studied the remanufacturing assembly combination matching method and established a matching model aiming at minimizing quality loss in product assembly. Xiao et al [8] studied the remanufacturing assembly inventory cost, proposed an assembly sequence optimization model based on the smallest assembly cost and the highest resource utilization, and used the network flow graph and mathematical linear programming method to solve the model. Zhu et al [9] developed a remanufacturing cost prediction model based on an improved BP neural network.…”

Research on process route optimization method based on improved hybrid genetic algorithm

“…Many scholars have combined heuristic intelligent optimization algorithm to carry out research. Xiao et al [3] aimed at the problem of high resource consumption and environmental emissions of blank design methods, The concept of work step unit is proposed to represent the characteristics of the workpiece. A low-carbon and low-cost multi-objective optimization model is established, and the relevant constraints are added.…”