Proceedings of the XMO Industrial Seminar 2026: Excellence in Manufacturing and Operations
Keywords
digital twin; human-robot collaboration; in-house manufacturing; resilient manufacturing; collaborative robotics; convergent manufacturing
Tracks
DIGITAL MANUFACTURING
DOI
10.5703/1288284318672
Abstract
Recent supply-chain disruptions and growing interest in domestic production have renewed attention to in-house manufacturing systems that are flexible, practical, and easier to deploy. Human-robot collaboration (HRC) is a promising option in such settings because it combines human adaptability and judgment with robotic precision and repeatability [1], [2]. Still, broader use of HRC in smaller or resource-constrained manufacturing environments is often limited by safety concerns, coordination challenges, and the cost and complexity of implementation. This paper presents a practical digital twin framework for safe human-robot collaboration in resilient in-house manufacturing. The contribution of the paper is not a generic three-layer architecture alone, but a lower-barrier workcell-level framework that combines standard industrial communication, explicit safety zoning, adaptive handoff support, and staged adoption for local manufacturing settings. A collaborative electromechanical assembly use case and a custom Python-based simulation study are used to illustrate how the framework can support safer operation, improved coordination, and more flexible in-house production. Preliminary simulation results from 1,000 matched trials indicate a 20.6% reduction in average cycle time, a 38.0% reduction in robot idle duration, and 95.6% near-conflict recall. To avoid overstating safety performance under rare conflict events, the evaluation also reports near-conflict precision, false-positive rate, false-negative rate, conflict-event base rate, and response latency. The work is relevant to convergent manufacturing because it combines digital-physical coordination with human-in-the-loop operation in a form that is practical for point-of-service and in-house production settings.
A Practical Digital Twin Framework for Safe Human-Robot Collaboration in Resilient In-House Manufacturing
Recent supply-chain disruptions and growing interest in domestic production have renewed attention to in-house manufacturing systems that are flexible, practical, and easier to deploy. Human-robot collaboration (HRC) is a promising option in such settings because it combines human adaptability and judgment with robotic precision and repeatability [1], [2]. Still, broader use of HRC in smaller or resource-constrained manufacturing environments is often limited by safety concerns, coordination challenges, and the cost and complexity of implementation. This paper presents a practical digital twin framework for safe human-robot collaboration in resilient in-house manufacturing. The contribution of the paper is not a generic three-layer architecture alone, but a lower-barrier workcell-level framework that combines standard industrial communication, explicit safety zoning, adaptive handoff support, and staged adoption for local manufacturing settings. A collaborative electromechanical assembly use case and a custom Python-based simulation study are used to illustrate how the framework can support safer operation, improved coordination, and more flexible in-house production. Preliminary simulation results from 1,000 matched trials indicate a 20.6% reduction in average cycle time, a 38.0% reduction in robot idle duration, and 95.6% near-conflict recall. To avoid overstating safety performance under rare conflict events, the evaluation also reports near-conflict precision, false-positive rate, false-negative rate, conflict-event base rate, and response latency. The work is relevant to convergent manufacturing because it combines digital-physical coordination with human-in-the-loop operation in a form that is practical for point-of-service and in-house production settings.