Accuracy design optimization of a CNC grinding machine towards low-carbon manufacturing. (20th June 2023)

• Record Type: Journal Article
• Authors: Wang, Liping; Han, Jihui; Ma, Fengju; Li, Xuekun; Wang, Dong
• Is Part Of: Journal of cleaner production. Volume 406(2023)
• Publication Date: 2023-06-20
• Subjects: Accuracy design -- Low carbon emissions -- Green manufacturing -- Multi-objective optimization -- Clean production; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2023.137100
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Accuracy design is an important and essential step for CNC machine tools, and traditional methods always focus on the accuracy optimization itself and ignore other objectives, such as low-carbon manufacturing. With the increasing concern about resources and the environment, energy consumption and carbon emissions should be taken into consideration in accuracy design optimization. This paper takes a novel CNC slider grinding machine with dual-head-dual-lead structure as an example to build an accuracy design model considering sustainable development. Specifically, the feed system of the machine tool is examined, which presents significant accuracy design challenges and has a great impact on the machine's positioning accuracy. By using the improved Sobol method, a sensitivity analysis is conducted to extract the critical geometric error terms, and the sensitivity indices of these terms are utilized to guide the optimization process. By considering manufacturing cost, quality loss, and carbon emissions of the grinding process with long period and high energy consumption, a more comprehensive accuracy design model is built and optimized by using the NSGA-II algorithm. Moreover, the Pareto optimal solution for balancing the objectives is selected through combining entropy-weight and technique for ordering preferences by the similarity of the ideal solution (TOPSIS). The optimization results demonstrate that the accuracy level of the machine tool can be ensured while reducing carbon emissions and total cost up to 30.2% and 18.4% respectively, which is important for promoting clean production in machine tool manufacturing. Besides, the proposed accuracy design framework has the potential to apply to other components and manufacturing processes of CNC machine tools to enhance sustainability and reduce carbon emissions. Highlights: Accuracy design optimization of machine tool considering sustainability. Sensitivity analysis of geometric error using the improved Sobol method. Reduction in carbon emissions while maintaining the desired accuracy.