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<p>Facing increasingly serious environmental challenges, low-carbon manufacturing has become a consensus in various industrial applications. Fused deposition modeling (FDM), as one of the most sustainable manufacturing methods, has become a vital task to tune the process parameters of FDM technology appropriately to reduce energy consumption minimizing any decline in part quality. To achieve this objective, an Energy Consumption and Part Quality Optimization (EPO) framework was proposed, and a case study was conducted on FDM to validate the effectiveness of the proposed framework. The EPO framework integrates four key functions: the perception and acquisition of relevant data and information, modeling of part quality and energy consumption, the selection of an appropriate multi-objective optimization algorithm, and the development of an intelligent optimization system. Subsequently, the mechanism energy consumption model and part quality modeling specific to FDM were given. Following this, the corresponding experiments were conducted to demonstrate the effectiveness of the proposed framework, in which the gray wolf optimization (GWO) algorithm and the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) were enhanced to improve the optimization outcomes. Ultimately, the optimal configuration of parameters was determined, with the nozzle temperature, printing velocity, and layer thickness for FDM set at 200 °C, 55 mm/s, and 0.33 mm, respectively. This configuration resulted in an 8.9% reduction in energy consumption during the printing process compared with the empirical scheme, indicating that both energy consumption and part quality can be effectively controlled, thereby supporting sustainable manufacturing. In comparison to the previous work, this study achieves greater energy savings while maintaining dimensional accuracy. Overall, the proposed method contributes to the development of a comprehensive collaborative optimization system that integrates energy consumption and various aspects of part quality across different materials, processes, and structures.</p>

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Optimization of energy consumption and dimensional accuracy for fused deposition modeling processes through a hybrid method

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Zhiqiang Yan,
  • Xu Guo,
  • Jizhuang Hui,
  • Jingxiang Lv,
  • Zhiguang Xu

摘要

Facing increasingly serious environmental challenges, low-carbon manufacturing has become a consensus in various industrial applications. Fused deposition modeling (FDM), as one of the most sustainable manufacturing methods, has become a vital task to tune the process parameters of FDM technology appropriately to reduce energy consumption minimizing any decline in part quality. To achieve this objective, an Energy Consumption and Part Quality Optimization (EPO) framework was proposed, and a case study was conducted on FDM to validate the effectiveness of the proposed framework. The EPO framework integrates four key functions: the perception and acquisition of relevant data and information, modeling of part quality and energy consumption, the selection of an appropriate multi-objective optimization algorithm, and the development of an intelligent optimization system. Subsequently, the mechanism energy consumption model and part quality modeling specific to FDM were given. Following this, the corresponding experiments were conducted to demonstrate the effectiveness of the proposed framework, in which the gray wolf optimization (GWO) algorithm and the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) were enhanced to improve the optimization outcomes. Ultimately, the optimal configuration of parameters was determined, with the nozzle temperature, printing velocity, and layer thickness for FDM set at 200 °C, 55 mm/s, and 0.33 mm, respectively. This configuration resulted in an 8.9% reduction in energy consumption during the printing process compared with the empirical scheme, indicating that both energy consumption and part quality can be effectively controlled, thereby supporting sustainable manufacturing. In comparison to the previous work, this study achieves greater energy savings while maintaining dimensional accuracy. Overall, the proposed method contributes to the development of a comprehensive collaborative optimization system that integrates energy consumption and various aspects of part quality across different materials, processes, and structures.