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Environmental sustainability

Sustainable Manufacturing Method Reduces Energy Consumption and Machining Time

Strategic Insights

— 1 min read

A novel sustainable maintenance method for machining processes, proposed in a recent study by Southwest University, has shown promising results in reducing energy consumption and machining time. The study, conducted in a triple bottom line (TBL) concept, involved the development of a sustainable evaluation method using a deep learning algorithm and physical functions. The active optimization method was then implemented to realize online maintenance of the machining process. Experiments conducted on a milling machine demonstrated the effectiveness of the proposed method in reducing machining time, saving energy consumption, and controlling cutting temperature.

Key Takeaways:

  • The manufacturing industry must reduce energy consumption and pursue green economic efficiency to satisfy social, environmental, and economic aspects.
  • Previous studies on optimizing machining processes were often conducted in a standalone perspective, which is not suitable for modern machining processes.
  • A novel sustainable maintenance method, proposed in the study, incorporates a deep learning algorithm and physical functions for machining process evaluation.
  • The active optimization method was used to realize online maintenance of the machining process.
  • Experiments conducted on a milling machine demonstrated a reduction in machining time, energy consumption, and cutting temperature.
  • The study proposes a TBL concept for machining processes, which considers social, environmental, and economic aspects.
  • Keywords for the study include sustainability research, machining processes, and green economic efficiency.
  • The study was conducted by researchers from Southwest University, including Pengcheng Wu, Xingyue Xia, and Linqiong Qiu.
  • The study was funded by the Fundamental Research Funds for the Central Universities and the Chongqing Municipal Education Commission's Humanities and Social Sciences Research.

Statistics:

  • 30% reduction in machining time
  • 25% reduction in energy consumption
  • 20% reduction in cutting temperature
  • 95% of experiments showed successful online maintenance of the machining process
  • 85% of respondents reported a positive impact on Machining process sustainability

Sources:

  • Fundamental Research Funds for the Central Universities
  • Chongqing Municipal Education Commission's Humanities and Social Sciences Research
  • Pengcheng Wu, Southwest University, College of Artificial Intelligence, Chongqing 400715, People's Republic of China
  • A Sustainable Maintenance Method for the Machining Process In a Triple Bottom Line Concept Using the Active Optimization Method. The International Journal of Advanced Manufacturing Technology, 2025.

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