Plug&Produce systems accept reconfiguration and have the attribute of physical and logical flexibility. To implement the Plug&Produce system in a manufacturing plant, there is a need to assure that the system is safe. The process of risk assessment provides information that is used to implement the proper safety measures to ensure human and machine safety. An important step in the risk assessment process is hazard identification. Hazard identification of Plug&Produce system is unique as the hazard identification method provided in the safety standards do not consider system flexibility. In this paper, a framework for hazard identification of a collaborative Plug&Produce system is presented. A study case that includes a collaborative Plug&Produce system is presented and the framework is applied to identify the system’s hazards. Also, the generalisation of the framework application is discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Plug & Produce is a modern automation concept in smart manufacturing for modular, quick, and easy reconfigurable production. The system’s flexibility allows for the configuration of production with abstraction, meaning that the production resources participating in a specific production plan are only known in the online phase. The safety assurance process of such a system is complex and challenging. This work aims to assist the safety assurance when utilizing a highly flexible Plug & Produce concept that accepts instant logical and physical reconfiguration. In this work, we propose a concept for online hazard identification of Plug & Produce systems, the proposed concept, allows for the detection of hazards in the online phase and assists the safety assurance as it provides the hazard list of all possible executable alternatives of the abstract goals automatically. Further, it combines the safety-related information with the control logic allowing for safe planning of operations. The concept was validated with a manufacturing scenario that demonstrates the effectiveness of the proposed concept.

Plug & Produce aims to revolutionize manufacturing by enabling seamless machine integration into production processes without extensive programming. This concept, leveraging multi-agent systems (MAS), offers increased flexibility and faster production ramp-up times after reconfiguration. As automated manufacturing moves towards greater human integration, this paper addresses safe operation within the Plug & Produce concept. The main safety challenge arises from autonomous decision-making, as agents in the MAS lack awareness of the risk consequences of their behavior. Additionally, the difficulty of perceiving the system’s exact behavior leads to the implementation of overly restrictive safety measures. This limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control to conduct automatic safety analysis and reason task allocations to avoid risks. The method’s benefits are the generation of control actions that comply with safety requirements during operation, eliminating the need for overly restrictive safety measures and allowing more effective equipment utilization.

The method’s benefit is illustrated through a manufacturing scenario with two different configurations: one using a hazardous machine and the other using a less hazardous one. Formal verification using the model checker NuSMV demonstrated that safety requirements were satisfied in both configurations, without the need for manual modifications of the safety control system after reconfiguration. The results for this specific manufacturing scenario showed that there are more reachable states (20 states) in the safer second configuration, compared to the first configuration (16 states). This means that the presented control strategy dynamically adjusts the system’s behavior to confirm safety. Hence, this method maintains safety without fixed safety rules that limit the operations.

Industry 5.0, which focuses on human-centric automation and utilizes advanced production technologies such as Reconfigurable Manufacturing Systems (RMS), requires manufacturers to prioritize workers’ well-being alongside efficiency. Addressing safety management in this evolving manufacturing paradigm is essential. However, ensuring safety in reconfigurable manufacturing often requires external outsourcing and increased man-hours. This leads to increased production costs and reduced flexibility due to the additional time required for safety assurance. Ideally, manufacturers seek safety management methods that leverage in-house expertise, reducing both production costs and time without compromising safety. Thus, a novel approach to safety management is necessary. This paper introduces a method for software-assisted safety management in RMS that leverages in-house competencies and streamlines safety validation after reconfiguration which enhances Industry 5.0’s adaptability. To empirically assess the proposed method, a conceptual software tool was developed and deployed to a reconfigurable Plug & Produce system for house wall fabrication within a laboratory setting. A usability test was performed to collect the man-hour needed for safety validation after reconfiguration using in-house competency. Analysis of the results revealed potential savings of 40% for one-off production and 35% for batches up to 5. While based on lab findings, they suggest cost reduction in real manufacturing. This empirical evidence underscores significant cost reduction potential in reconfigurable manufacturing, highlighting its role in promoting flexibility, economical sustainability, and human-centricity within Industry 5.0 © 2024 IEEE.

Modern smart manufacturing requires flexible and safe coordination between human operators and autonomous agents. Current multi-agent system planning often relies on reactive safety mechanisms, leading to inefficiencies and workflow interruptions. Existing approaches typically overlook runtime safety policies during plan generation, producing functionally valid but operationally inefficient plans. As a result, avoidable slowdowns and emergency stops occur when humans enter shared workspaces, reducing both throughput and predictability.

This research introduces a safety-aware planning approach that builds on an enhanced agent ontology by integrating agent negotiation with automated planning. A dynamic map of enhanced edges is constructed, where each edge represents a sequence of skills and its aggregated runtime execution cost. During runtime, agents negotiate to exclude unsafe edges and update costs based on runtime conditions, enabling the solver to generate plans that are both safe and efficient. Evaluation in a Plug & Produce case study with two system configurations demonstrated consistent advantages over a baseline reactive safety policy. The proposed planner, increased throughput by 50-80%, reduced execution costs by 20-55%, and lowered variability by up to a factor of 17. By integrating safety awareness into the planning stage, the approach improves efficiency, robustness, and compliance with human safety standards while supporting adaptable reconfigurable manufacturing.

This work advances the vision of a human-centric future manufacturing by demonstrating that smart manufacturing systems can reason about operator presence to avoid hazards without compromising productivity.