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Currently, manufacturing enterprises realizing their machining processes strive to address Sustainability Development Goals (SDGs), especially SDG 9 to build sustainable industrialization. Integration artificial intelligence (AI) into production enable companies to increase productivity while reducing environmental impacts. However, monitoring and forecasting the level of sustainable development (SD) remains an area requiring further attention, as there is still a research gap in the study of the transformation of machining process into sustainable machining process (SMP). This systematic literature review analyzes a total of 8,884 articles published between 2023 and September 2025. Ultimately, 71 research articles were included in the final analysis and categorized into three thematic clusters using VOSviewer software, based on author keywords extracted from the ScienceDirect and Springer databases, and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) methodology and the Systematic Literature Review (SLR) approach. The research findings emphasize that AI-based technologies support SDG 9 achieving in the context reducing energy consumption and carbon emissions, improving machining performance and enabling the monitoring of changes in machining process parameters: surface roughness, depth, feed rate, and speed. Furthermore, the key challenges associated with this transformation have been identified and discussed.

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Systematic Review of AI-based Models for Sustainable Machining Process

  • 会议论文

  • 发表日期：

    2026年

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  • Hanna Łosyk,
  • Małgorzata Szmołda,
  • Justyna Patalas-Maliszewska

摘要

Currently, manufacturing enterprises realizing their machining processes strive to address Sustainability Development Goals (SDGs), especially SDG 9 to build sustainable industrialization. Integration artificial intelligence (AI) into production enable companies to increase productivity while reducing environmental impacts. However, monitoring and forecasting the level of sustainable development (SD) remains an area requiring further attention, as there is still a research gap in the study of the transformation of machining process into sustainable machining process (SMP). This systematic literature review analyzes a total of 8,884 articles published between 2023 and September 2025. Ultimately, 71 research articles were included in the final analysis and categorized into three thematic clusters using VOSviewer software, based on author keywords extracted from the ScienceDirect and Springer databases, and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) methodology and the Systematic Literature Review (SLR) approach. The research findings emphasize that AI-based technologies support SDG 9 achieving in the context reducing energy consumption and carbon emissions, improving machining performance and enabling the monitoring of changes in machining process parameters: surface roughness, depth, feed rate, and speed. Furthermore, the key challenges associated with this transformation have been identified and discussed.