Customized products and low-volume production are becoming more popular resulting in a problem for dedicated manufacturing systems that are designed for mass production. Adapting a system to new demands is expensive and requires many products to be produced before it becomes a reasonable investment. This has forced factories to use human workers for manufacturing tasks that often change. This thesis focuses on a concept called Plug & Produce, which makes it easier to move, add, and remove resources in manufacturing systems. This is done by containing resources in process modules that all have the same physical connectors. To handle the control of the manufacturing system a multi-agent system is considered where each part to be produced for products has a part agent software running that represents that part. Each part agent takes care of their own manufacturing goals by communicating with resource agents that control the resources in the system. In this thesis, a Plug & Produce framework is describedthat consists of a configurable multi-agent system, together with a configuration tool for defining agent behaviours. Methods for identifying the resource that has been connected to a Plug & Produce system are investigated. Communication between agents in Plug & Produce is investigated. Scheduling is described for the presented systems to avoid conflicts when running multiple agents. Also, a pathfinding method for Plug & Produce is presented, which automatically gathers the necessary information for finding paths to transport parts through the manufacturing system.

This thesis presents methods for simplifying the use of multi-agent systems in Plug & Produce. The demand for customized products and low volume production is constantly increasing. The industry has for many years used dedicated manufacturing systems where it is difficult and expensive to adapt to new product designs. Instead, factories are forced to use human workers for certain tasks that demand high flexibility and rapid adaption for new product designs. Several solutions have been proposed over the years to create highly flexible automation systems that automatically handles rapid adaption for new products. A concept called Plug & Produce aims at creating a system where resources and parts can be added in minutes rather than days in dedicated systems. One promising solution for implementing Plug & Produce is the distributed approach called multi-agent systems, where each resource and part get its own controller that communicates with each other to reach manufacturing goals. The idea is that the system automatically handles the adaption for new products. However, still today the use of such systems is extremely limited in the industry. One reason is the lack of mature multi-agent systems that are easy to use and that hides the complexity of the underlying agent system from the users. This is a huge problem since these systems tend to be more complex than traditional approaches. Thus, this thesis focuses on simplifying the use of multi-agent systems by proposing various methods for bringing the multi-agent technology for Plug & Produce closer to the industry.

Today, multi-agent systems are still uncommon in the industry because they require more time to be implemented than traditional manufacturing systems. In this paper, a conceptual model and guidelines are defined for communication and negotiation between agents for Plug & Produce systems. Standards for agent communication exists today, such as the FIPA collection of specifications. However, FIPA is a broad and general standard for any kind of system and leaves a lot of room for interpretation. This paper presents a new conceptual model and guidelines on how to simplify the implementation phase by limiting the choices an engineer must make when implementing a multi-agent system for a manufacturing system. © 2020 The Authors.

Multi-agent technology, used for implementing Plug & Produce systems have many proposed benefits for fast adaption of manufacturing systems. However, still today multi-agent technology is not ready for the industry, due to the lack of mature supporting tools and guidelines. The result is that today, multi-agent systems are more complicated and time-consuming to use than traditional approaches. This hides their true benefits. In this paper, a new method for configuring agents is presented that includes automated deployment to manufacturing systems and by its flexible design opens the possibility to connect many other supporting tools when needed. A configuration tool is also designed that works with the proposed method by connecting to an agent configuration database. The overall aim of the method is to simplify the steps taken for adapting a manufacturing system for new parts and resources.  

This paper describes a method together with an implementation for automating the detection, identification and configuration of newly added resources and parts in a Plug and Produce system using OPC UA. In a Plug and Produce system, resources and parts are usually controlled by agents, forming a multi-agent system of collaborating resources. Hence, when a resource or part is connected to the system, a corresponding agent must be instantiated and associated with that specific device. In order to automate this, the system needs information about newly connected devices. This information could, for example, be positional data describing where the device is connected. Some devices like tools and parts to be processed have no own network connection, but still, they should get an agent with correct configuration instantiated. In this work, OPC UA is used for communication between devices and the corresponding agents. All agents and their communication are handled by an Agent Handling System, consisting of an OPC UA HUB together with functions for device detection and agent instantiation. The HUB is used for transferring data between devices and their agents in the network by OPC UA protocols. When a device is connected to the network, it is detected, and a connection is automatically created to the HUB that becomes configured for transmitting data between the device and its corresponding agent. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

This article presents a framework for Plug & Produce that makes it possible to use configurations rather than programming to adapt a manufacturing system for new resources and parts. This is solved by defining skills on resources, and goals for parts. To reach these goals, process plans are defined with a sequence of skills to be utilized without specifying specific resources. This makes it possible to separate the physical world from the process plans. When a process plan requires a skill, e.g., grip with a gripper resource, then that skill may require further skills, e.g., move with a robot resource. This creates a tree of connected resources that are not defined in the process plan. Physical and logical compatibility between resources in this tree is checked by comparing several parameters defined on the resources and the part. This article presents an algorithm together with a multiagent system framework that handles the search and matching required for selecting the correct resources.

This paper presents a method for automating the generation, verification and deployment of robot programs used in prefabrication of walls for family houses. The making of robot programs is today performed manually by experts, i.e. implying high costs. This is a huge disadvantage since each wall can be unique. The work demonstrates, with implementation and testing, a method to automate the generation of robot programs for fabrication of walls made of wood. This includes the task of generating collision free paths, automatic verification of path performance and deploying to a real industrial robot with minimal human interaction. © 2018 The Authors. Published by Elsevier B.V.

The ongoing trend towards manufacturing of customized products generates an increased demand on highly efficient work methods to manage product variants through flexible automation. Adopting robots for automation is not always feasible in low batch production. However, the combination of humans together with robots performing tasks in collaboration provides a complementary mix of skill and creativity of humans, and precision and strength of robots which support flexible production in small series down to one-off production. Through this, collaboration can be used with implications on reconfiguration and production. In this paper, the focus and study is on designing safety for efficient collaboration operator—robot in selected work task scenarios. The recently published ISO/TS 15066:2016 describing collaboration between operator and robot is in this context an important document for development and implementation of robotic systems designed for collaboration between operator and robot.

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 recent years the manufacturing sectors are migrating from mass production to mass customization. To be able to achieve mass customization, manufacturing systems are expected to be more flexible to accommodate the different customizations. The industries which are using the traditional and dedicated manufacturing systems are expensive to realize this transition. One promising approach to achieve flexibility in their production is called Plug & Produce concept which can be realized using multi-agent-based controllers. In multi-agent systems, parts and resources are usually distributed logically, and they communicate with each other and act as autonomous agents to achieve the manufacturing goals. During the manufacturing process, an agent representing a robot can request a path for transportation from one location to another location. To address this transportation facility, this paper presents the result of a futuristic approach for an online path planning algorithm directly implemented as an agent in a multi-agent system. Here, the agent systems can generate collision-free paths automatically and autonomously. The parts and resources can be configured with a multi-agent system in the manufacturing process with minimal human intervention and production downtime, thereby achieving the customization and flexibility in the production process needed. 

In this paper, a path planning algorithm is designed and tested with a real robot for a Plug & Produce demonstrator. The demonstrator is divided into modules that can be connected and removed. Modules are used for various processes like tool change and storage. This paper focuses on the process of cutting-tool change for the production industry. The Plug & Produce demonstrator uses a multi-agent system where parts and resources are agents. A part agent, e.g., a cutting-tool, can request a robot to perform skills like transportation. This requires the robot to be autonomous. The aim of this paper is to automate the path planning for industrial robotics in a Plug & Produce system. This is done by implementing a sampling based RRT algorithm combined with a collision detection function in RobotStudio. With various real time scenarios, the path planning execution time is observed and presented in the paper.