The study contributes to how transdisciplinary research collaboration can be designed to address the complexity of the human-technology nexus in the context of Industry 5.0 and to identify incentives from the manufacturing industry to engage in transdisciplinary research efforts. Engaged scholarship and work-integrated learning approaches are applied to integrate diverse research disciplines and active stakeholder engagement to address complex societal challenges. The methodology of this research is a qualitative case study, including workshops and focus groups with a project consortium of eight companies, with industry experts and university researchers. Findings contribute to transdisciplinary research collaboration viewed as an iterative continuous process including three phases for the process of reaching full potential of transcending disciplines and organizations. Contribution shows that industry highlights the need to address human challenges in smart technology adoption, motivating engagement in transdisciplinary research. Advancing smart manufacturing requires embracing creativity and innovation in the human-technology nexus. Further, transdisciplinary research collaboration needs to be based on trust, relationships, sharing, courage, mutual understanding and respect for each other's disciplines and expertise. The collaborative design accentuates the significance of transdisciplinary research in university-industry collaboration when moving forward with human-centric and smart manufacturing in line with the evolving Industry 5.0 paradigm

Adapting automated manufacturing to accommodate new product designs is often challenging in traditional manufacturing systems. Plug & Produce is a concept that enables for faster adaptation to new product requirements. Research has shown that this can be done through standardized, modular resources that can be easily reconfigured and moved around. However, new flexible approaches are needed to make these modules work together with the automation control system. Typically, these changes to automation requires re-programming or advanced reconfigurations. This article presents an approach using Large Language Models to simplify the steps to instruct a Plug & Produce system on what to do.

The aim is to make use of inhouse knowledge, rather than external experts for adapting the manufacturing system to new product designs. It is identified that simulations are important tools for evaluating the generated instructions before deploying them to a physical system. The proposed system is implemented and the result in this article shows that automated manufacturing can be adapted by using a natural language.  

The demand for customized products in a saturated market of trendy customers forces the manufacturing industries to transform their manufacturing from a high volume of uniformed products to low volumes and a high mix of products. High mix and low volume manufacturing is most often manually performed since existing automation solutions are only profitable for mass manufacturing, due to explicitly designed control software where the product data is implemented as low-level control code. Highly flexible automated manufacturing systems such as Plug & Produce are requested, but challenges still exist before industrial implementation. This article proposes a digitally configurable system where data for new or modified products data is configured from the perspective of the product and its manufacturing processes instead of the manufacturing resources. In a Plug & Produce system, process modules with manufacturing resources are easy to replace for new or modified products and possibly to duplicate if higher capacity is needed. Configurable multi-agent systems are proposed by several researchers as a control system for Plug & Produce. An agent is a piece of autonomous computer code that negotiates with other agents and concurrently solves tasks, distributed on parts and resources. A part is a part of a product and part agents handle manufacturing goals for the parts. Resource agents know their capability and start operating as soon as they are plugged in. Resource agents follow pluggable process modules containing manufacturing resources and act as drivers for the modules. Gantry robots have by design a naturally orthogonal coordinate system and most often lack the functionality to handle work and tool coordinate objects as standard industrial robots do. Work objects refer to a base coordinate system and tool objects contain a reference to the tool center point. These references are in this article integrated into resource agents together. A place coordinate agent has the global perspective of the Plug & Produce cell and provides the process modules with reference coordinates of the place they are plugged into. Coordinates are recalculated from a product perspective into a resource perspective by coordinate transformations built into the skills of resource agents. This structure enables the possibility for process planners in the manufacturing company to make changes on a daily basis. A test with a gantry robot Plug & Produce demonstrator was performed and presented in this article to verify the generic structure of the gantry robot control system into agents. 

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.

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.

This article presents a systematic literature review on the Plug and Produce concept in advanced automated manufacturing control systems. Over recent decades, this concept has evolved significantly, with researchers focusing on enhancing its applicability and improving its conceptual, logical, and physical aspects across various sub-areas such as system design, methodologies, and supporting tools within the Industry 4.0 and Industry 5.0 frameworks. The review offers technical insights on the research domain of Plug and Produce accompanied by an analytical schematic outlining five key evolving research streams ranging from system design framework, and functionality features, up to the empirical application. Additionally, the article discusses important issues surrounding the evolution of Plug and Produce in alignment with emerging trends within Industry 5.0 automation. By analyzing the literature and current trends in industrial automation, the article highlights critical key development directions for shaping the future of manufacturing systems focusing on smart, circular, and human-centric solutions using Plug and Produce. © 

Digital twin technology is pivotal in the transition of manufacturing industries towards Industry 4.0, as it enables the creation of virtual representations of physical shop floors and production processes. This technology addresses manufacturing challenges by allowing the reuse and adjustment of production equipment in real-time, facilitating novel technologies, and supporting the adoption of flexibilities to add new product variants. The significance of resource-efficient and flexible production systems is highlighted by their ability to optimize resource utilization and enable reconfiguration through digital models. This study specifically investigates the differences between physical systems and their digital twins, focusing on the sustainable updating of virtual models of a flexible automation cell. Digital models of the flexible automation cell are acquired using 3D laser scanning techniques, capturing data as point clouds. The differences between new point cloud models and existing digital models are analyzed using CloudCompare software. Identified changes are extracted from the digital models as point clouds and converted into 3D mesh models through surface reconstruction techniques, thereby updating the digital twin. To address inaccuracies in the detailed extraction of digital models compared to physical models, an additional fusion step is implemented. This step integrates data from photogrammetry and 3D laser scanning, enhancing the point clouds and producing accurate 3D models of the automation cell. The main focus of this study is to determine the most effective approach for scanning an automation cell and identifying changes by comparing two digital models, thereby contributes to the field of digital twin technology with a novel methodology for sustainable virtual model updates.  

This paper presents a model-based safety software that performs Hazard Identification (HI) for Plug&Produce (P&P) systems automatically. P&P systems, inspired by Plug&Play in computers, aim to integrate devices and tools into the manufacturing system with minimum integration efforts and costs. When plugging a new resource, it will exchange all the required information with the manufacturing system and be ready to operate within minutes rather than days or weeks. One of the challenges that face this concept is performing proper risk assessment after each change in the system. Therefore, the risk assessment needs to be automated as much as possible. This paper is about automating one risk assessment step: Hazard Identification. A new safety model is designed to identify hazards. The presented software analyses this model by implementing a novel algorithm that uses lookup tables to cover various possible hazards when resources work together. This software will support the risk reduction team by drastically reducing the time needed for HI and being ready for the next steps in risk analyses. Automating identifying hazards is an essential step towards automating the entire risk assessment process and achieving safe P&P systems.

The need for human-centric perspectives on smart automation are increasing as new technological advancements and global societal changes continuously re-shapes the manufacturing industry. Meeting this need is challenging and cannot be accomplished by one sole field of research expertise and requires university-industry collaboration. The research presented combines expertise from different disciplines, i.e., industrial engineering, automation and control, business administration, management, informatics, and work-integrated learning. The research group has extensive experience of such collaborations and is presently applying previous research and experiences in studies of human-centric smart automation striving to build unique research. Transdisciplinary research offers many opportunities; however, challenges include, combining methodologies, communication jargon, mutual respect for different disciplines and designing joint research studies. The research presented addresses such challenges by taking a transdisciplinary and collaborative approach bringing forth the human-centric perspective when advancing smart robotic automation. The aim is to exemplify and illustrate how to design transdisciplinary research in collaboration with industry for knowledge exchange and co-creation of new knowledge. The collaborative design emphasises the value of a transdisciplinary approach in university-industry collaboration when studying, understanding, and evolving the human-centric perspective of technological advancement in the manufacturing industry. Findings contribute design for synergizing technology development and manufacturing management to reach human-centric smart automation. The implication of the research relates to broader societal issues aligned with Industry 5.0, placing humans at the centre when introducing novel production processes and new technologies.

This article proposes a Plug & Produce and goal-oriented configurable multi-agent system that admits adding and removing resources to balance the manufacturing capacity without doing any digital reconfiguration or reprogramming. To handle that a new part-agent strategy is developed and described. Goals are central in designing autonomous multi-agent systems, possibilities to execute goals in parallel are desirable when the process requirements admit concurrent use of resources. Also, a standardized graphical method, the sequence of goals chart, is proposed to define and visualize parallel and sequential goals independently of available resources. Premanufacturing of wooden houses belongs to one of many manufacturing industries that claim flexible automation systems due to the high degree of customized products and a fluctuating market. A physical Plug & Produce robot-based workstation was built up to verify the flexibility in altering capacity and adoption to product modifications of a house wall section. Further, the simplicity of modifying the proposed configurable multi-agent system was compared to more traditionally designed systems and plain multi-agent systems with superior results. The flexibility is built into the proposed system by default as a part of the concept, simple enough to be handled by existing in-house knowledge within manufacturing companies.