AI-Driven Product-Production-CoDesign through digital manufacturing change management in PLM systems

Contemporary manufacturing faces unprecedented challenges driven by accelerated time-to-market demands, stringent quality standards, and mass customization requirements. This complexity necessitates seamless integration between product design and production planning, particularly within Product Lifecycle Management (PLM) systems, that current change management approaches lack. This research presents an AI-driven architecture for change management in PLM systems, specifically addressing Product-Production-Co-Design challenges. The proposed framework integrates Machine Learning algorithms with cyber-physical production systems to enable real-time adaptations to design modifications while maintaining manufacturing efficiency and sustainability considerations. Through data-driven analysis of existing PLM workflows, critical bottlenecks in change propagation are identified and targeted for automation. The methodology applies AI-based predictions to anticipate the manufacturing implications of design changes. This enables proactive adjustment of production systems, parts and processes, reducing waste and improving resource utilization - key factors in design for sustainability. ... mehrThe cyber-physical architecture facilitates bidirectional communication between design and production domains, ensuring that manufacturing constraints inform design decisions while design changes are seamlessly integrated into production planning. Results demonstrate significant improvements in change response time and manufacturing adaptability both in the short and long run. The AI-enhanced system reduces manual intervention in change management processes by up to 98% for instance in deriving the MBOM and BOP from the EBOM. This research contributes to the advancement of intelligent manufacturing systems that support sustainable production practices through optimized Product-Production-CoDesign methodologies, establishing a foundation for future autonomous manufacturing environments.