Shenyang University of Technology, Tiexi District, No.111, Shenliao West Road, Shenyang, China.
Environ Sci Pollut Res Int. 2024 Aug;31(40):53156-53176. doi: 10.1007/s11356-024-34633-8. Epub 2024 Aug 23.
Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.
机床是工业部门的骨干，代表着全球最大的设备库存。每台机床生产过程中的碳排放值得关注。有效管理机床制造过程中的碳排放至关重要。本文提出了一种机床制造过程中早期碳排放量预警的新方法，利用自适应动态指数加权移动平均（EWMA）方法。该方法解决了识别和监测不确定和动态相关性下异常碳排放量的挑战。利用动态采样技术和自适应原理，该方法构建了自适应动态 EWMA 控制图。EWMA 控制图包含一个多目标优化设计模型，侧重于机床制造过程中的碳排放量，并包含统计、经济和环境目标。为了缓解复杂控制图多目标优化算法中收敛速度慢和优化精度差的问题，本研究提出了一种改进的哈里斯鹰优化（HHO）算法作为求解算法。最后，通过对五轴机床（EOC）制造过程中的碳排放量进行监测，对该方法进行了应用验证。结果表明，该方法对异常碳排放量具有快速响应能力，并能及时发出警报。这进一步证实了所提出方法的有效性和可行性。最终，该方法为机床行业实现环保和高质量增长提供了一种可行的策略。
