Scholarlink

Enhancing robotic system efficiency through lean principles is essential in the dynamic landscape of industrial automation. This study introduces a comprehensive framework combining the “Visionary Pathway Design Tool” (VPDT), simulation-based analysis, and TRIZ methodologies to optimize lean concept integration in robotic systems across various production scenarios. The research addresses the complexities of designing intelligent industrial robotic systems under lean management, focusing on enhancing production process resilience and efficiency The approach integrates VPDT's AI-driven problem definition and Ideal Final Result (IFR) visioning capabilities with simulation models developed in tools like RoboDK. It leverages predictive modeling and scenario planning to envision ideal operational states and uses backcasting to map strategic pathways to achieve these goals. TRIZ principles are applied to resolve inherent contradictions in system design, ensuring innovative solutions aligned with lean management objectives. The simulation phase, using real-world industrial scenarios, and reinforcement learning assesses the impact of lean strategies on production process resilience.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Lean Robotics in Industrial Applications Through VPDT, Simulation, and TRIZ Integration

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Eyas Deeb,
  • Stelian Brad

摘要

Enhancing robotic system efficiency through lean principles is essential in the dynamic landscape of industrial automation. This study introduces a comprehensive framework combining the “Visionary Pathway Design Tool” (VPDT), simulation-based analysis, and TRIZ methodologies to optimize lean concept integration in robotic systems across various production scenarios. The research addresses the complexities of designing intelligent industrial robotic systems under lean management, focusing on enhancing production process resilience and efficiency The approach integrates VPDT's AI-driven problem definition and Ideal Final Result (IFR) visioning capabilities with simulation models developed in tools like RoboDK. It leverages predictive modeling and scenario planning to envision ideal operational states and uses backcasting to map strategic pathways to achieve these goals. TRIZ principles are applied to resolve inherent contradictions in system design, ensuring innovative solutions aligned with lean management objectives. The simulation phase, using real-world industrial scenarios, and reinforcement learning assesses the impact of lean strategies on production process resilience.