Increasing demand for rapid product variation and customization requires automation that can be reconfigured efficiently without extensive reliance on external expertise. Part-specific information is typically embedded in application programs of traditional control systems, making adaptation to new products complex and time-consuming.

The contributions of this thesis are a humancentric, configurable, multi-agent control system at the machine level for nextgeneration Plug & Produce. The agent's intelligence is enhanced by utilizing automated AI planning for transfer tasks. Part-dependent data are relocated from control logic to product configurations and are performed from the part and process perspectives rather than from programming resource functionalities. Manufacturers extract part data from CAD models and incorporate process knowledge via graphical user interfaces, enabling them to configure systems for new products without specialized programming skills.

An approach that opens the door for creative operators to handle the next-generation, human-centric Plug & Produce system. Integrating agents into robot control systems and other external control systems via device primitives makes these systems flexible and independent of part-specific data. A new part agent strategy allows concurrent resources to be plugged in to balance capacity. Low-level controls imply high realtime demands, which are met through short negotiation processes via abstract interfaces that match real interfaces on resource agents, efficient agent communication, and fast automated AI planners. 

Denna doktorsavhandling presenterar ett koncept som är avsett att öka flexibiliteten i framtidens Plug & Produce-system för automatisk tillverkning. Behovet av flexibla och omställbara automationslösningar är stort, då snabba förändringar krävs för att möta en alltmer dynamisk marknad med växande efterfrågan på nya och unika produkter.

Automation bidrar till lägre kostnader genom att minska behovet av arbetskraft, samtidigt som den säkerställer jämn kvalitet, minimerar förslitningsskador och skapar en säkrare och mer trivsam arbetsmiljö genom att öka avståndet mellan operatör och tillverkningsprocess. Att ersätta högflexibel manuell produktion med automatiserade lösningar är dock en betydande utmaning. Som ett svar på detta presenteras en ny generation av konceptet Plug & Produce, ett system som kan ställas om av den tillverkarnas egen personal, för att snabbt och enkelt anpassas till nya produkter och ordersituationer. Operatörernas roll blir mer kreativ när de själva kan anpassa produktionen av nya artiklar och balansera kapaciteten. Traditionellt utvecklas automationssystem av ingenjörer för ingenjörer, med fokus på den tekniska konstruktionen av utrustningen, vilket ofta kräver extern expertis vid förändringar. Genom att konfigurera automationen utifrån produkterna och deras tillverkningsprocesser, förflyttas kompetensbehovet till ett område där de tillverkande företagen redan besitter expertis.  Så mycket information som möjligt extraheras direkt från produktens digitala design. Processkunskap tillförs via grafiska användargränssnitt som styr tillverkningsflödet och hanterar specifika parametrar såsom mått, bearbetningshastigheter, temperaturer och färg.

Plug & Produce-system är uppbyggda kring inpluggningsbara resurser. Till exempel kan en modul med en robot pluggas in för att samarbeta med befintlig robot och därmed öka produktionskapaciteten. Specialdesignade resurser kan pluggas in för att öka effektiviteten och minimera energikonsumtionen. För att den inpluggade resursen självmant skall börja jobba och samarbeta med övriga resurser är den försedd med egen lokal artificiell intelligens som kan förhandla fram vilken resurs som är mest lämpad för en uppgift. Resurserna kan också automatiskt planera för verktygsbyten och sidoflöden såsom materialtransporter. Dessa resurser kan tack vare sin intelligens pluggas in i olika Plug & Produce-system och är därmed återvinningsbara i nya system, vilket gagnar miljön. På samma sätt förses produkterna med intelligens och kallas då för smarta produkter. Dessa produkter har som mål att bli färdigproducerade genom delmål i form av tillverkning och monteringsprocesser.

Plug & Produce-testbäddar har byggts upp tillsammans med representanter från tillverkningsindustrin för att verifiera teorierna. Bland annat en testbädd för väggsektioner till prefabricerade trähus samt en testbädd som plockar ihop material i kitt som skickas ut till monteringslinor. Båda testbäddarna är exempel på applikationer där hög flexibilitet krävs då variationen av det som skall tillverkas eller kittas är stor.  

The manufacturing industry faces a shortage of skilled workers and an increasing demand for customized products, making traditional automation unsuitable for the emerging high-mix, low-volume production. There is a growing need for intelligent automation that enables in-house knowledge to perform frequent reconfigurations. A promising solution involves distributed intelligence, where products autonomously manage their goals, and modular resources possess the necessary skills to achieve them. Process planners aim to digitally configure manufacturing processes and sequencing without relying on detailed, device-specific programming. This article validates a configurable multi-agent-based control system that employs Artificial Intelligence-driven transport planning using Satisfiability Modulo Theories (SMT), ensuring cost-effective adaptation to new parts, materials, and resources. A Plug & Produce robot station, equipped with tools, tool holders, and related components for a kitting application, is utilized for validation. This study compares automatically planned tool changes with the complexity of traditional Programmable Logic Controller (PLC) and robot programming in on-demand manufacturing.

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.

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. 

Industries of low-batches or one-of manufacturing aim for automation that is competitive enough to adapt to new or modifed products daily through in-house knowledge that focuses on manufacturing processes and not on machine function programming. To solve this, a complete set of actions that utilize seamless data transfer from product design in CAD to a Plug & Produce automation concept is proposed together with a generic structure for the integration of standard industrial robots into agents. This structure enables agents to handle their local reference coordinate systems and locations relative to a global perspective. Seamless utilization of data from product designs to Plug & Produce will simplify and shorten the time of digital development through concurrently usable text-based and graphical confguration tools of a confgurable multi-agent system. Needed data extracts directly from the product design as requirements of operational goals. Extraction of data from the product design, sequence of goals, and process plans, which are recipes of how to solve goals, can by this concept be confgured by in-house knowledge that has the process knowledge but not necessarily programming competence.

Adaptations to a fluctuating market and intensified customer demands for unique products are a challenge for manufacturers. Manual manufacturing is still the most flexible, nevertheless, automation ensures stable quality, minimizes wear and tear of the operators, and contributes to a safer and better working environment as the distance between the operator and the process can be increased and screened off. Hence, the manufacturing industry is searching for human-centric automation solutions that are flexible enough to handle these challenges. Conventional automation is tailored for one or a few similar variants of products, in addition, increased flexibility implies increased complexity to handle. This licentiate thesis demonstrates a flexible Plug & Produce automated manufacturing concept where the complexity is redirected to focus on the products and manufacturing processes by utilizing artificial intelligence. Together with digital threads that connect the product design to automatic manufacturing that enables manufacturing companies to manage new production scenarios with their in-house knowledge. Data is picked directly from the computer-based design of the products and process knowledge that normally exists within the manufacturing company is added through graphical user interfaces. The graphical configuration tools visualize the flow of sequential and parallel manufacturing operations together with process-bound information. Plug & Produce relies on pluggable process modules with re-cyclical manufacturing resources that can be plugged in and out as needed. As an example, a module with a robot can be plugged in to help an existing robot and thereby balance the production capacity. In Plug & Produce resources start working and cooperate with other resources automatically when they are plugged in. To achieve this, the resources are provided with distributed artificial intelligence together with intelligent products that know how to be finalized. In this concept, everything is digitally configurable by the in-house knowledge of the manufacturing companies. A Plug & Produce test bed was built to verify the concept in cooperation with industrial representatives.

Denna licentiatavhandling påvisar ett koncept för att öka flexibiliteten och samtidigt rikta om komplexiteten i automatiserade produktionssystem hos tillverkande företag på ett sätt så att deras interna personal på egen hand kan ställa om tillverkningen mot nya produkter. Anpassningar till marknadens fluktuationer och efterfrågan av nya unika produkter är en ständigt pågående process. Alltmer av produktionen flyttas tillbaka till Sverige och övriga Europa vilket ökar efterfrågan på flexibel och omställbar automation. Automation håller nere prisnivån då arbetskraften är dyr, säkerhetsställer jämn kvalité, minimerar förslitningsskador på de anställda och bidrar till säkrare och trevligare arbetsmiljö då distansen mellan operatör och process kan ökas och avskärmas. Produktion som flyttas till hemmamarknaden från låglöneländer ersätter ofta högflexibel och anpassningsbar manuell tillverkning vilket är en stor utmaning för industrin. Ett Plug & Produce koncept för automatiserad tillverkning utvecklas och beskrivs i denna avhandling där automationen enkelt kan ställas om av den interna personalen och anpassas till nya produkter. Omställning med hjälp egen personal möjliggörs genom att så mycket information som möjligt utvinns från produktens datorbaserade design. Processkunskap som normalt besitts inom det tillverkande företaget adderas därtill med hjälp av grafiska användarinterface som visar flödet av tillverkningsoperationer tillsammans med processpecifika uppgifter såsom mått, bearbetningshastigheter, temperaturer och färg. Plug & Produce system är uppbyggda kring processmoduler med tillverkningsresurser som kan pluggas in och ut efter behov. Till exempel kan en modul med en robot pluggas in för att avlasta befintlig robot och därmed öka produktionshastigheten. Specialdesignade resurser kan pluggas in för att öka effektiviteten och minimera energikonsumtionen. För att den inpluggade processmodulen självmant skall börja jobba och samarbeta med de andra modulerna är den försedd med egen lokal artificiell intelligens. Dessa processmoduler kan tack vare sin intelligens pluggas in i olika Plug & Produce system och är därmed återvinningsbara i nya system. Intelligensen kan vara lokalt placerad i en dator på resursen eller i datormolnet kopplat till resursen. På samma sätt kan produkterna förses med intelligens och kallas då för smarta produkter. Dessa produkter har som mål att bli färdigproducerade genom delmål i form av tillverkningsoperationer. Denna intelligens förses med kunskap och erfarenheter av personalen inom det tillverkande företaget genom användarvänliga interface. När användarvänligheten Plug & Produce testbädd har byggts upp tillsammans med representanter frånprefabricerade trähusindustrin. Tillverkning av prefabricerade trähus är i idag ihög grad manuell då existerande automationslösningar inte är flexibla nog eftersom husen är i hög grad är kundanpassade. Arbetet som beskrivs i denna avhandling gynnar trähusindustrin och därmed klimatet då trä binder kol för en lång tid framåt.

Manual assembly systems are featured with high flexibility but with the risk of lower quality, higher cycle time, inefficient resource employment, and affecting sustainability goals in comparison to fully automated ones. Conventional automated assembly is challenged by the desired level of flexibility when compared to what automation through Plug & Produce system represents. Plug and Produce, during the last few decades aimed at addressing highly flexible automation systems handling rapid changes and adaptations as one dominant solution. Multi-agent System (MAS) as a tool to handle different areas of manufacturing control systems can be used in Plug & Produce representing every physical control entity (e.g., parts, resources) as agents. This article aims to describe a model-based configurable multi-agent design in Plug and Produce system together with a prototype implementation of the actual automated assembly use case of a kitting operation highlighting flexibility and reconfigurability and the model functionality. A model-based approach with a few models using UML standards describes the structure and behavior of the system. Model instantiation is introduced and followed by real prototype use case implementation. The use case study of advanced automated kitting operation in the assembly automation domain has been selected. Agent-based operation control systems have been applied during the assembly process. The evaluation was accomplished by testing several scenarios on Plug & Produce for kitting operation. To conclude, several desirable functionality features of the framework during the demonstration such as rapid instantiation and adaptation, and in particular, the flexibility features have been examined and evaluated with several failure-handling testing scenarios. © 2023 IEEE.

Mass customization has become more attractive but requires a transformation towards more flexible solutions in contrast to dedicated manufacturing systems. Flexibility includes complex tasks such as the introduction of new products or new manufacturing processes as well as to efficiently handle daily balancing. The main challenge when it comes to flexibility in manufacturing is to be able to handle the technical aspects and still be competitive. In this article we consider the cost for flexibility to include two main things; (1) setup time, e.g., time for planning, design, programming and configuration, installation, ramp-up, scrapping of old equipment, preparation of facility, hardware installation, and (2) need of competence, inhouse knowledge, external competence, or external expert competence. This article presents an overview of available solutions and the level of flexibility and the level of competence that is needed for a reconfiguration one can expect out of a specific solution. Further, most of the existing solutions found do not consider or address the full problem of flexibility. However, we describe a possible future of industrial concept: Plug & Produce, which can address flexibility within manufacturing more completely and sustainably over time. Methods for configuration instead of programming are developed by University West. 

In a flexible manufacturing industry the production planner may need to make an updated description of the control strategy every day. The description contains all possible routing paths and is based on actual circumstances. It varies depending on, e.g., rebalancing due to market changes, scheduling of available operators, introduction of new parts, and rerouting due to a machine break down or planned service. A Part oriented Sequence of Operation (P-SOP) description language has been formulated to assist the production planner to be able to handle these flexible manufacturing scenarios. Multi-agents to control the manufacturing are automatically generated from the P-SOP description language. The P-SOP agent generator creates IEC 61131-3 PLC code that can be executed on standard PLC’s. An agent consists of a head, a communicator and a body. The head and the communicator are the automatically generated part with a predefined interface against the physical body, e.g., the mechanical/electrical structure of a robot. This feature eliminates the need for an external expert in PLC programming. The head contains many small sub-sequences for all operations that are defined for the specific body. The purpose of the communicator is to communicate with surrounding neighbour agents to form a multi-agent system. The formulated language and the P-SOP agent generator has been successfully tested and evaluated in an industrial environment.