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<p>Machining operations represent 83% of manufacturing energy consumption and contribute 19% of global greenhouse gas emissions, yet existing approaches treat environmental optimization separately from performance metrics. This paper introduces an integrated eco-feature framework that semantically enriches CAD entities by integrating geometrical, machining, and environmental data to simultaneously enhance quality, productivity, and sustainability. The methodology employs a two-level optimization process: (i) parametric optimization using Taguchi-Grey Relational Analysis to balance cutting time, surface roughness, and material removal rate, and (ii) sustainable optimization through the Part/Tool/Machine/Fluid (ParTMF) architecture to assess environmental impacts. A decision-support system based on the integration of the AHP and PROMETHEE methods allows machining scenarios to be ranked according to several criteria. As a proof of concept, the proposed framework is applied to a case study involving the machining of a pocket with simple geometry. The results show a 3% improvement in the Grey Relational Grade (a normalized multi-criteria index) compared to the initial configuration, while maintaining surface quality. This data-driven framework provides a practical basis for embedding sustainability considerations into design and process-planning phases.</p>

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Development of an action model focused on environmental enhancement incorporating eco-features in machining

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Hadhami Benslama,
  • Sami Chatti,
  • Mehdi TLIJA,
  • Abdelmajid Benamara,
  • Raoudha Gaha

摘要

Machining operations represent 83% of manufacturing energy consumption and contribute 19% of global greenhouse gas emissions, yet existing approaches treat environmental optimization separately from performance metrics. This paper introduces an integrated eco-feature framework that semantically enriches CAD entities by integrating geometrical, machining, and environmental data to simultaneously enhance quality, productivity, and sustainability. The methodology employs a two-level optimization process: (i) parametric optimization using Taguchi-Grey Relational Analysis to balance cutting time, surface roughness, and material removal rate, and (ii) sustainable optimization through the Part/Tool/Machine/Fluid (ParTMF) architecture to assess environmental impacts. A decision-support system based on the integration of the AHP and PROMETHEE methods allows machining scenarios to be ranked according to several criteria. As a proof of concept, the proposed framework is applied to a case study involving the machining of a pocket with simple geometry. The results show a 3% improvement in the Grey Relational Grade (a normalized multi-criteria index) compared to the initial configuration, while maintaining surface quality. This data-driven framework provides a practical basis for embedding sustainability considerations into design and process-planning phases.