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<p>Machine vision enables automated quality control, process monitoring, and robotic operations in manufacturing. While adoption is increasing, small- and medium-sized enterprises (SMEs) often face barriers such as limited resources, lack of technical expertise, and standards that do not address their specific needs. This research develops a conceptual framework for SME-oriented machine vision integration. Key requirements were identified through a systematic literature review and expert interviews. A morphological matrix maps these requirements against existing standards and research, forming the basis of a UML-modeled framework. The framework is implemented as a Model Context Protocol (MCP) server, enabling structured information retrieval via generative AI tools. Validation via a focus group highlighted the framework’s usability, relevance, and coverage. Results provide a foundation for supporting SMEs in adopting machine vision and point to future research opportunities, particularly in enhancing generative AI for interactive automation.</p>

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A conceptual framework for machine vision integration in manufacturing SMEs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Jonas Werheid,
  • Johannes Zysk,
  • Aymen Gannouni,
  • Anas Abdelrazeq,
  • Robert H. Schmitt

摘要

Machine vision enables automated quality control, process monitoring, and robotic operations in manufacturing. While adoption is increasing, small- and medium-sized enterprises (SMEs) often face barriers such as limited resources, lack of technical expertise, and standards that do not address their specific needs. This research develops a conceptual framework for SME-oriented machine vision integration. Key requirements were identified through a systematic literature review and expert interviews. A morphological matrix maps these requirements against existing standards and research, forming the basis of a UML-modeled framework. The framework is implemented as a Model Context Protocol (MCP) server, enabling structured information retrieval via generative AI tools. Validation via a focus group highlighted the framework’s usability, relevance, and coverage. Results provide a foundation for supporting SMEs in adopting machine vision and point to future research opportunities, particularly in enhancing generative AI for interactive automation.