Research on digital twin technology for green manufacturing establishes new frameworks for energy efficiency assessment, while practical experiences in environmental planning and waste management inform the founding of Green Bridge Sustainable Solutions, an enterprise connecting U.S. and international sustainability partnerships through bidirectional academic-industry knowledge transfer.
-- Green transformation in manufacturing is becoming a global focus. To address shortcomings in current single-dimensional energy evaluation methods, the study proposes a digital twin-based assessment approach. The method uses BOM and ECBOM data to integrate energy consumption characteristics across virtual and physical spaces throughout the manufacturing lifecycle. Research results show that this approach improves the accuracy of energy efficiency assessment, with BOM and ECBOM serving as key data links. The framework offers manufacturers a unified method for quantifying energy use across physical and virtual environments.
The study constructs a five-dimensional energy-efficiency evaluation model that integrates physical entities, virtual entities, service systems, data fusion processes, and interconnection relationships within a digital twin framework. By using BOM and ECBOM as data links, the method supports the extraction, visualisation, and real-time application of energy consumption characteristics throughout the manufacturing service lifecycle. The framework enables a more accurate assessment of energy use in different stages and provides a basis for optimising manufacturing processes.
Evaluation results show that the ECBOM model can reveal clear differences in energy consumption across components, allowing high-consumption links to be accurately identified. The data indicates that the spindle box body consumes 14.06 kilograms of standard coal equivalent and produces 20.12 kilograms of carbon dioxide, significantly higher than other parts. By converting production information from multiple BOM views into unified energy metrics, the method supports a comprehensive assessment of each production link and offers a basis for improving energy use across manufacturing processes.
Contributing to this research is Bin Li, who is pursuing a Master of Arts in Climate and Society at Columbia University and holds a Bachelor of Science in Sociology from the University of California, Riverside. He has worked at Suzhou Walin New Energy Technology Co., Ltd., the Bureau of Ecology and Environment, the Environmental Planning Institute, and Dongqiao Centralised Sewage Treatment Co., Ltd., where he gained experience in pollution investigation, water quality analysis, environmental impact assessment, sustainability initiatives, sewage management, and ecological restoration. His academic background also includes case studies on the Bay Area Clean Air Plan and U.S. EPA regulations, supported by technical skills in Python, R, SQL, and data visualisation.
The study demonstrates how a digital twin–based framework supports comprehensive energy consumption assessment. By establishing a five-dimensional model and a multi-view BOM-to-ECBOM conversion method, the research provides structured mechanisms for quantifying energy use throughout the manufacturing service lifecycle. These contributions enhance the accuracy of energy efficiency evaluation and offer a foundation for optimising high–energy consumption processes within manufacturing systems.
Contact Info:
Name: Bin Li
Email: Send Email
Organization: Bin Li
Website: https://scholar.google.com/citations?user=MIKy_RQAAAAJ&hl=en
Release ID: 89178773
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