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.

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.

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.  

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. 

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.

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.

A current trend in production is to reduce energy consumption where possible not only to lower the cost but also to be a more energy efficient entity. This paper presents an energy model to estimate the electrical energy consumption of 2D-belt robots used for material handling in multi-stage sheet metal press lines. An estimation of the energy consumption is computed by the proposed energy model based on the robot components’ specifications, the robot path and trajectory. The proposed model can predict the energy consumption offline by simulation, and thus, before installation, avoiding the need for physical experiments. It is demonstrated that it can be used for predicting potential energy reductions achieved by optimising the motion planning. Additionally, it is also shown how to investigate the energy saving achieved by using mechanical brakes when the robot is idle. This effectively illustrates the usefulness of the proposed energy model. © 2020 Inderscience Enterprises Ltd.

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 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.