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.

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.  

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.

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/)