From Rigid Teach-In to Adaptive manufacturing— flexible, Stable, Cost-Effective.
Variations in part orientation, frequent product changes, and an increasing mix of variants push traditional, hard-coded robotics to its limits: manual corrections, high maintenance costs, and unstable cycle times are the result.
Physical AI specifically supplements proven automation logic with sensor technology and AI—exactly where the process requires situational adaptation, without having to redesign the entire system.
Current Challenges
- Changing part orientations and variants lead to quality variations.
- Manual interventions and changeovers delay production.
- Teach-in becomes a constant task even for minor changes.
- Early hardware decisions increase costs and risk.
Key Factors for Getting Started
- Hybrid approach: stable steps handled classically, dynamic subtasks handled adaptively.
- Digital proof of concept: test virtually before investing in hardware.
- Clear use case with KPIs for productivity, stability, and quality.
- Open, vendor-neutral architecture and reusable software components.
- Role-based operating concepts and a concise maturity assessment.
The white paper “Physical AI in Manufacturing: A Practical Guide to Getting Started with Adaptive Robotics” shows how to identify suitable initial use cases, build a digital proof of concept, and progress through five stages to a pilot—including architectural principles and a KPI framework for economic evaluation.