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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.

The purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To find a suitable algorithm and verify that it can be used online in a Plug & Produce system, a comparative study between various existing sampling-based path planning algorithms was conducted. Much research exists on path planning carried out offline; however, not so much is performed in online path planning. The specific requirements for Plug & Produce are to generate a path fast enough to eliminate manufacturing delays, to make the path energy efficient, and that it run fast enough to complete the task. The paths are generated in a simulation environment and the generated paths are tested for robot configuration errors and errors due to the target being out of reach. The error-free generated paths are then tested on an industrial test bed environment, and the energy consumed by each path was measured and validated with an energy meter. The results show that all the implemented optimal sampling-based algorithms can be used for some scenarios, but that adaptive RRT and adaptive RRT* are more suitable for online applications in multi-agent systems (MAS) due to a faster generation of paths, even though the environment has more constraints. For each generated path the computational time of the algorithm, move-along time and energy consumed are measured, evaluated, compared, and presented in the article., CC BY 4.0

Increased environmental awareness is driving the manufacturing industry towards novel ways of energy reduction to become sustainable yet stay competitive. Climate and enviromental challenges put high priority on incorporating aspects of sustainability into both strategic and operational levels, such as production scheduling, in the manufacturing industry. Considering energy as a parameter when planning makes an already existing highly complex task of production scheduling even more multifaceted. The focus in this study is on inverse scheduling, defined as the problem of finding the number of jobs and duration times to meet a fixed input capacity. The purpose of this study was to investigate how scheduling can be formulated, within the environment of discrete event simulation coupled with reinforcement learning, to meet production demands while simultaneously minimize makespan and reduce energy. The study applied the method of modeling a production robot cell with its uncertainties, using discrete event simulation combined with deep reinforcement learning and trained agents. The researched scheduling approach derived solutions that take into consideration the performance measures of energy use. The method was applied and tested in a simulation environment with data from a real robot production cell. The study revealed opportunities for novel approaches of studying and reducing energy in the manufacturing industry. Results demonstrated a move towards a more holistic approach for production scheduling, which includes energy usage, that can aid decision-making and facilitate increased sustainability in production. We propose a conceptual framework for scheduling for minimizing energy use applying discrete event simulation as an environment for deep reinforcement learning., The work was carried out at the Production Technology Centre at University West, Sweden supported by the Swedish Governmental Agency for Innovation Systems (Vinnova) under the project SmoothIT and by the KK Foundation under the project Artificial and Human Intelligence through Learning (AHIL). Their support is gratefully acknowledged. Assistance provided by Master's students Maria Vincenta Vivo and Mohammadali Zakeriharandi was greatly appreciated.

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 was written as part of the PoPCoRN project, funded by the K-K foundation and the Miljö för Flexibel och Innovativ Automation, Project reference: 20201192, Funded under: Europeiska regionala utvecklingsfonden/VGR.Distributed under the termsof the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 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 work was supported by Miljö för Flexibel och Innovativ Automation under Project no. 20201192 funded by the Europeiska regionala utvecklingsfonden/VGR. Paper no. TII-20-0219.Creative Commons Attribution 4.0 License

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.

Plug and produce demonstrators handles multiple processes in the industry, appropriate path planning is essential and at the same time there is an increasing emphasis on more sustainable processes. To ensure the sustainability and automate these processes optimized path planning is required. We present an implementation of a path planning algorithm, which creates a smooth collision free path and considers energy use. In the paper, we demonstrated the implementation of PRM (Probabilistic Road Map) path planning and Dijkstra based optimization algorithm in a simulation environment and thereafter test in a real plug and produce demonstrator. To validate the simulated results the real energy was measured through the signal analyzer online. The measured results outlined in this paper includes; computational time, move along path time, and energy use with different loads. From the experiments and results we conclude that the combination of the two algorithms, PRM with Dijkstra, can be used to generate a collision free optimized path. Here we have considered the distance as the cost function for Dijkstra optimization algorithm and measured the energy of the collision free optimized path. The practical implication of this research is as an enabler for any kind of application where there are large variations of orders e.g., kitting techniques in assembly operations for manufacturing industry., The work was carried out at the Production Technology Centre at University West, Sweden supported by the Swedish Governmental Agency for Innovation Systems (Vinnova) under the project named SmoothIT and KK Foundation under the project named PoPCoRN. Their support is gratefully acknowledged.

Optimized path planning of robots are necessary for the industries to thrive towards greater flexibility and sustainability. This paper proposes an implementation of a collision-free path with the shortest distance. The novelty of the work presented is the new ARRT*(Adaptive Rapidly exploring Random Tree Star) algorithm, which is modified from the RRT*(Rapidly exploring Random Tree Star). In a constraint environment, RRT* algorithms tend to fail when searching for suitable collision-free paths. The proposed ARRT* algorithm gives an optimized feasible collision-free paths in a constraint environment. The feasibility to implement RRT* and ARRT* in a Multi Agent System as a path agent for online control of robots is demonstrated. We have created a digital twin simulated environment to find a collision-free path based on these two algorithms. The simulated path is tested in real robots for feasibility and validation purpose. During the real time implementation, we measured the following parameters: the algorithm computation time for generating a collision-free path, move along time of the path in real time, and energy consumed by each path. These parameters were measured for both the RRT* and the ARRT* algorithms and the test results were compared. The test results were showing that ARRT* performs better in a constrained environment. Both algorithms were tested in a Plug and Produce setup and we find that the generated paths for both algorithms are suitable for online path planning applications., The work was carried out at the Production Technology Centre at University West, Sweden supported by the Swedish Governmental Agency for Innovation Systems (Vinnova) under the project named SmoothIT and KK Foundation under the project named PoPCoRN. Their support is gratefully acknowledged.

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.

Currently, Scania is not offering full air suspended AWD trucks, which it seems to be a demand for. This study acts as a first step to fulfill this demand. Including, a benchmarking of what competitors offer as well as an investigation and an evaluation of Scania’s current suspension system. Moreover, a requirement specification and a concept generation for a front air suspension system on AWD trucks have been presented. Eight concept were generated, where two were chosen for further study of design, force analysis and roll gradient analysis. It was concluded that there is a market for this configuration, however, implementing it would require extensive design work., I nuläget erbjuder inte Scania luftfjädring för samtliga hjulaxlar på AWD lastbilar, vilket det tycks finnas ett kundbehov av. Denna studie agerar som ett initialt steg till att uppfylla detta kundbehov. Studien inkluderar inledningsvis en analys av vad konkurrenter erbjuder samt en undersökning och utvärdering av Scanias nuvarande fjädringssystem. Utöver det, har en kravspecifikation och en konceptgenerering för främre luftfjädring på AWD lastbilar presenterats. Åtta stycken koncept genererades, varav två stycken valdes för vidare studie av design, kraftanalys samt krängstyvhetsanalys. Slutsatserna var att det finns en marknad för denna typ av konfiguration, dock skulle det behövas omfattande designarbete för att implementera det.