D.Y. Yu

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.

Multi-objective optimization of machining parameters in multi-pass turning operations for low-carbon manufacturing:

Most recent studies on machining parameter optimization in machining operations focused on reducing machining cost and energy consumption. However, environmental impacts caused by manufacturing activities were not involved in those studies, which can be quantified by equivalent carbon dioxide emissions. In this study, a direct method was proposed to quantify the carbon emissions generated during multi-pass turning operations. Moreover, machining parameter optimization models of multi-pass turning operations in dry and wet cut environments were established using an experimental design method. Three objectives were considered in both models: carbon emissions, operation time, and machining cost. Furthermore, a multi-objective teaching–learning-based optimization algorithm was used to deal with the models. The optimization results indicated that the use of cutting fluids could significantly reduce carbon emissions and machining cost and improve production efficiency in multi-pass turning operations.

A novel artificial ecological niche optimization algorithm for car sequencing problem considering energy consumption

This article proposes a novel artificial ecological niche optimization algorithm for car sequencing problem considering energy consumption to satisfy the requirements of the paint shop and the assembly line. Energy consumption, which is an important issue, is often neglected during the production process. In this article, the mathematical model of car sequencing problem with energy consumption is established. A new artificial ecological niche optimization algorithm that imitates natural ecosystem is proposed to solve this problem. The car sequence is considered as an ecosystem. Ecological niche theory, niche overlap and niche differentiation principles are applied in this algorithm, which leads the ecosystem to adapt itself to a more ecological balance condition accompanying less violence appear in the car sequence. Energy consumption in the scheduling process of ring sorting buffer was considered as biogenic migration in the algorithm. The proposed algorithm is verified on a set of instances provided by the French car manufacturer Renault. The computation results indicate the effectiveness of the proposed algorithm.