• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2026, Vol. 62 ›› Issue (1): 361-373.doi: 10.3901/JME.260026

Previous Articles    

Acquisition of Life Cycle Inventory for Manufacturing Process of Machine Tools Subjected to Multi-scenario

XIAO Xingyuan1,2, WANG Liming1,2, WANG Xiaoguang1,2,3, LI Fangyi1,2, LI Jianfeng1,2,4, NIE Yanyan4, LIU Weitong1,2, WANG Yitong1,2, MA Yan1,2, WANG Boyun1,2, CUI Yuqi1,2   

  1. 1. School of Mechanical Engineering, Shandong University, Jinan 250061;
    2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061;
    3. China Machinery Industry Federation, Beijing 100010;
    4. Engineering Training Center, Shandong University, Jinan 250002
  • Received:2025-01-08 Revised:2025-07-03 Published:2026-02-13

Abstract: The complexity and diversity of machine tool processing scenarios, along with the irregular diffusion and strong time variability of environmental emissions during the manufacturing process, lead to significant challenges in obtaining real-time inventory data. These challenges result in severe data gaps in the environmental emission inventory of manufacturing processes, thereby constraining environmental impact analysis and green improvement efforts in machine tool processing. To address these issues, a machine tool processing unit process scenario model is constructed based on the framework of “processing equipment, processing object, auxiliary materials, and process parameters,” enabling a unified representation of machine tool processing scenario information. On this basis, an environmental emission inventory data collection system is developed using the Internet of Things (IoT) architecture, which facilitates real-time collection, transmission, processing, and storage of inventory data. A strategy for environmental emission data collection and an inventory calculation model for multi-scenario machine tool processing are proposed, allowing for the acquisition and conversion of emission data under complex working conditions. Finally, the electrical discharge wire-cutting machine tool processing is taken as an example, and the influence of scenario attributes on environmental performance is explored through orthogonal experiments, leading to the determination of an optimal process scheme. The feasibility and effectiveness of the proposed method are thereby validated. This research provides a systematic solution for real-time collection, modeling, and traceability of environmental emission inventory data in machine tool processing, offering quantitative foundational data and decision-making references for green process evaluation, optimization, and green transformation in the field.

Key words: green manufacturing, machine tool processing, scenario modeling, environmental emissions, inventory data

CLC Number: