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.
Collaborative robotic systems where human(s) and robot(s) cooperate in performing a common task is an attractive solution to introduce automation combined with high flexibility for tasks that have a high complexity and characterized by low volume or down to one-off. By introducing collaboration in robotics systems, the operator can complement with cognitive capacity and skill in order to gain in flexibility and agility in the task operation. This paper describes on-going work related to work on collaboration between operator and robot. User scenarios are outlined together with methods, software components and hardware to support collaboration, where some of these are under development. As the standards related to collaborative robotic systems are soon to be completed, it is expected that this type of semi-automatic systems will be important for flexible and agile automation of production which otherwise cannot be automated.
The latest state-of-the-art Computer Aided Production Engineering (CAPE) simulation technology offers OPC integration for PLC verification. A critical drawback with this technology has been identified and described within this paper. A new time synchronisation method and a simulation architecture are therefore presented and proposed. The time synchronisation method together with the architecture can be used when verifying and developing real-time dependent control logic for industrial control system, e.g. PLC with CAPE tools. The method described in this paper is general and should work on any PLCs that are compatible with the IEC 61131-3 standard. A test case was also carried out, showing that by disregarding time synchronisation it is impossible to verify real-time dependent PLC functions together with CAPE tools in a reliable way. However, the test case also shows that by applying the proposed time synchronisation method together with the described simulation architecture a successful industrial verification method is achieved
Flexible automation cells with rapid product changes are an important competitive advantage for industries today. These cells can increase a company’s productivity and thereby increase their profits. A flexible cell shall be able to handle different products with none or minimal changes to the cell itself. A powerful tool, which can be used to analyse and verify such cells, is discrete event system simulation. Problems such as potential bottlenecks, deadlocks, answers to "what-if" questions and the level of resource utilisation can be gathered. The drawback of discrete event system simulation is that the modelling task is both time consuming and difficult to accomplish. Furthermore, state-of-the-art discrete event system simulation tools that are used in the industry today are not suitable for flexible automation. If the production scenario is changed, e.g. introduction of a new product, the simulation and modelling has to be redone and this is both time consuming and tedious. In this paper a new approach will be presented that enables discrete event simulation models to be generated automatically. The models are generated from information retrieved from a PLM/PDM database system, which is shared among other engineering tools such as robot simulation, CAD and process planning. Hence, when the cell and the database are updated a new model can easily be generated. The database is also connected to the real cell so up-to-date data can be retrieved from the real cell. The model generator described in this paper was implemented and tested in a discrete event system simulation tool and showed promising results. With this approach it is possible to handle flexible automation cells more effectively in a process planning stage.
This paper describes a general virtual manufacturing concept for industrial control systems. Our virtual manufacturing concept provides a distinct advantage; programming, verification and optimisation of complex real-time dependent control functions described by real control code, which can be directly transferred to the real manufacturing system. To achieve this distinct advantage, a time synchronised virtual manufacturing system is a necessity. The aim of this paper is thus to present and to describe in detail, our proposed virtual manufacturing concept. To the authors’ knowledge no such general virtual manufacturing concept, i.e. one that can correctly handle complex real-time dependent control functions, currently exists. To summarise previous work related to virtual manufacturing and industrial control systems, several critical issues have been identified. The virtual manufacturing concept proposed in this paper addresses these issues. To verify that our concept can manage these critical issues found and further is suitable in industrial applications a virtual manufacturing test case is also presented. The test case, that includes motion control (i.e. servo), complex control functions, real control systems etc., was carried out with success.
Simulation based PLC code verification is a part of virtual commissioning, where the control code is verified against a virtual prototype of an application. With today’s general OPC interface it is easy to connect a PLC to a simulation tool for e.g. verification purposes. However, there are some problems with this approach that can lead to an unreliable verification result. In this paper, four major problems with the OPC interface are described, and two possible solutions to the problems are presented: a general IEC 61131-3 based software solution, and a new OPC standard solution
This paper presents a system for full automation of free-form-fabrication of fully dense metal structures using robotized laser melting of wire. The structure is built of beads of melted wire laid side by side and layer upon layer governed by synchronized robot motion. By full automation is here meant that the process starts with a product specification of a component, and ends in a geometrically validated dense metal component fulfilling industrial material requirements. Due to the complexity of this flexible manufacturing system, a number of different disciplines are involved. This paper discusses mainly the system design, which includes how off-line programming is used for automatic generation of code and how feedback control is used for on-line adjustment of parameters based on desired building properties. To meet industrial needs, the project is carried out in a close cooperation between research and development activities in academy and industry.
This study presents work in progress on how to develop a process-planning tool to handle interaction between human operators and robots within a robot cell. First, we introduce how to include human activities in the process flow; then, we turn to our ideas for communication and feedback systems inside a robot cell. A small example of how to design interactive and re-programmable screens is presented.
Holonic Manufacturing System (HMS) is an integrated multi-agent technology that represents the developing direction in the automation field. HMS can be represented as a non-hierarchical manufacturing architecture with no or limited supervision. However, it is a challenge for a single holon in a non-hierarchical system to make globally optimal decisions. This paper presents a simulation-based optimization method for HMS by introducing a new wizard holon. A wizard holon collects the necessary information from the entire HMS and uses Discrete Event System (DES) simulation to evaluate the cost of different decisions. Since a non-hierarchical approach is used the wizard input is only treated as an advice to achieve more globally optimal decisions. The decisions are still taken by the local holon. Even for an experienced operator it might be hard to predict the outcome of a decision in a critical situation. Hence, wizard advices are valuable for all types holons, including machines, robots, and operators.
This study is part of a research project aims at off-line programming and verification of industrial control systems. In this paper, an off-line method for press line throughput rate optimization and control system verification is proposed, implemented and evaluated. The main tool is a virtual press station, developed by the first author, consisting of an emulated control system for a feeder/extractor robot which communicates with 3D-simulated production equipment. Moreover, several virtual press stations have been coupled and synchronized in a virtual press line. An important feature of the system is that the virtual robot controller is emulated, yielding an exact representation of the control logic and the possibility to run the entire system in virtual real time. The application considered is a sheet metal forming process where it is difficult to achieve maximum capacity utilization. There is much to gain if the control logic is improved and the throughput rate is increased. For this purpose, an automated robot motion optimization method is implemented and evaluated, using the virtual press line.
This article proposes a classification of different methods for validation, off-line programming and optimisation of control logic. The classification is an overview of different methods available and includes advantages and disadvantages for each method. The method overview points out a superior method, control system emulation, which is the most cost-effective and flexible method. The control system emulation method is also general and may be applied to validate and optimise control logic in various applications. Further, the method is compared with several other methods for validation of industrial control systems. However the method requires a standardised system architecture. This article proposes such architecture for the control system emulation method. Here, a control system emulator has also been implemented with the specific system architecture described in this article. An application case is also provided to demonstrate an approach to the integration of a control system emulator into a virtual manufacturing system.
Due to constant changesin the market there is a need for low-cost and low-volume manufacturing.Usually this type of production is difficult to automate due to the time ittakes to become profitable and the inflexibility of such solutions. Therefore, flexible automation solutions needto be addressed together with cost effective aspects. In this paper, a newconcept for the design of a flexible, robotized solution based on leanautomation is presented and simulated. The proposed lean automation concept isformed of standardized robot stations, human-robot collaboration and costeffective level of automation. The main goals are flexible automated productionsystem and reduced production cost. This paper shows that the proposed flexiblelean automation concept has some key advantages compared to the traditionalrobot cells; a longer lifetime for the robot cell as well as being easier tore-balance, introduce new parts to and expand the cell. Further, it also showsthat the proposed concept reduces the cost for automation of products with low volume.
Due to constant changes in the market there is a need for low-cost and low-volume manufacturing. Usually this type of production is difficult to automate due to the time it takes to become profitable and the inflexibility of such solutions. Therefore, flexible automation solutions need to be addressed together with cost effective aspects. In this paper, a new concept for the design of a flexible, robotized solution based on lean automation is presented and simulated. The proposed lean automation concept is formed of standardized robot stations, human-robot collaboration and cost effective level of automation. The main goals are flexible automated production system and reduced production cost. This paper shows that the proposed flexible lean automation concept has some key advantages compared to the traditional robot cells; a longer lifetime for the robot cell as well as being easier to re-balance, introduce new parts to and expand the cell. Further, it also shows that the proposed concept reduces the cost for automation of products with low volume.
The aim of this paper is to describe on-going work in the integration of humans in automated manufacturing systems. The intention is to achieve a flexible manufacturing system to meet the rapid developing and changing of today’s industry. The approach is based on a control concept with multi-agents. Humans, which are considered as a valuable factor in industrial production, are proposed as flexible agent resources for the automated manufacturing system.
A test case was performed on a manufacturing system where three different groups of humans where integrated in the system; inspection, carrier and recovery. The P-SOP agent generator was used to automatically generate IEC 61131-3 PLC control code for the system.
Job shop scheduling problems are computationally complex combinatorial optimization problems. Genetic algorithms have been used in various forms and in combination with other algorithms to solve job shop scheduling problems. A partially flexible job shop with precedence constraints increases this complex behaviour. There are two main parts to optimizing ajob shop, the routing and the scheduling. The objective here is to get consistent optimal makespan using a genetic algorithm. This paper firstly, presents a simulation approach for the considered partially flexible job shop scheduling problem. Which take into account the precedence constraints and reduce situations of deadlock. To solve the partially flexible job shop scheduling problem a genetic algorithm was used and improved. It utilise a genetic crossovers for routing and a new random shuffle feature is introduced for the scheduling. The computational results have shown that the algorithm performs well in terms of finding a consistent optimal schedule for the given problem
Metal Deposition (MD) is a rapid prototyping technique to build parts by depositing metal in a required fashion. When a complex-shaped part is to be built, a simulation tool is needed to define robot trajectories. Three different simulation-based methods for robot trajectory generation are introduced and compared in this study. The methods are; reversed milling, adapted rapid prototyping and application programming in a computer aided robotics software. All methods were shown capable of creating robot paths for complex shapes, with the CAR software approach being the most flexible. Using this method, the geometry to be built is automatically sliced into layers and a robot path is automatically generated. The method was tentatively evaluated and appears to provide a powerful technique in the design and optimisation of robot paths for MD. Experiments showed that it is possible to manufacture fully dense parts using an Nd-Yag laser.
The need for human-centric perspectives on smart automation are increasing as new technological advancements and global societal changes continuously re-shapes the manufacturing industry. Meeting this need is challenging and cannot be accomplished by one sole field of research expertise and requires university-industry collaboration. The research presented combines expertise from different disciplines, i.e., industrial engineering, automation and control, business administration, management, informatics, and work-integrated learning. The research group has extensive experience of such collaborations and is presently applying previous research and experiences in studies of human-centric smart automation striving to build unique research. Transdisciplinary research offers many opportunities; however, challenges include, combining methodologies, communication jargon, mutual respect for different disciplines and designing joint research studies. The research presented addresses such challenges by taking a transdisciplinary and collaborative approach bringing forth the human-centric perspective when advancing smart robotic automation. The aim is to exemplify and illustrate how to design transdisciplinary research in collaboration with industry for knowledge exchange and co-creation of new knowledge. The collaborative design emphasises the value of a transdisciplinary approach in university-industry collaboration when studying, understanding, and evolving the human-centric perspective of technological advancement in the manufacturing industry. Findings contribute design for synergizing technology development and manufacturing management to reach human-centric smart automation. The implication of the research relates to broader societal issues aligned with Industry 5.0, placing humans at the centre when introducing novel production processes and new technologies.
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.
Optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C3. The results illustrate that with C3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.
Simulation-based optimisation carries the burden of computationally expensive fitness calculations. It is very often used to tackle large-scale optimisation problems with a relatively high level of complexity. Therefore, it is of interest to have optimisation techniques dedicated to simulation-based optimisation. This paper proposes a simulation-based optimisation approach, called Constructive Cooperative Coevolutionary (C3) search procedure, to optimise the control of interacting production stations. An optimisation algorithm is embedded in the C3 search procedure to optimise subproblems separately. It includes a novel constructive heuristic that creates a feasible solution for the considered problem efficiently. It also incorporates an extended version of the existing cooperative coevolutionary method that can handle large-scale optimisation problems. Furthermore, this paper presents a case study considering a sheet metal press line as an example of interacting production stations. In this case study, the performance of the proposedC3 search procedure is evaluated and compared with other optimisation algorithms. This shows that the C3 search procedure is able to successfully optimise the press line within a given number of fitness calculations, outperforming existing algorithms. Also, it is shown that C3 can be embedded with either stochastic or deterministic optimisation algorithms, without sacrificing performance.
Production that incorporates robotics consumes energy and the trend today is to reduce consumed energy not only to lower the cost but also to be a more energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This paper presents an energy model to predict the energy consumption of 2D-belt robots used for press line tending. Based on the components’ specifications and the trajectory, an estimation of the energy consumption is computed. The capabilities of the proposed energy model to predict the energy consumption during the planning-phase (i.e. before installation), avoiding the need for physical experiments, are demonstrated. This includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. 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 purpose and usefulness of the proposed energy model.
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.
Simulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical time frame is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line’s production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors’ knowledge the first time a press line has been optimised efficiently in this way.
This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.
The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.
This article investigates multi-objective optimization of the robot trajectories and position-based operation-coordination of complex multi-robot systems, such as press lines, to improve the production rate and obtaining smooth motions to avoid excessive wear of the robots’ components. Different functions for handling the multiple objectives are evaluated on realworld press lines, including both scalarizing single-objective functions and Pareto-based multi-objective functions. Additionally, the Multi-Objective Constructive Cooperative Coevolutionary (moC3) algorithm is proposed, for Pareto-based optimization, which uses a novel constructive initialization of the subpopulations in a co-adaptive fashion. It was found that Paretobased optimization performs better than the scalarizing single-objective functions. Furthermore, moC3 gives substantially better results compared to manual online tuning, as currently used in the industry. Optimizing robot trajectories and operation-coordination of complex multi-robot systems using the proposed method with moC3 significantly improves productivity and reduces maintenance. This article hereby addresses the lack of systematic methods for effectively improving the productivity of press lines.
Determining the control parameters for sheet metal press lines is a large scale and complex optimisation problem. These control parameters determine velocities, time constants, and cam values of critical interactions between the equipment. The complexity of this problem is due to the nonlinearities and high dimensionality. Classical optimisation techniques often underperform in solving this kind of problems within a practical timeframe. Therefore, specialised techniques need to be developed for these problems. An existing approach is simulation-based optimisation, which is to use a simulation model to evaluate the trial solutions during the optimisation. In this paper, an efficient simulation-based optimisation algorithm for large scale and complex problems is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. Hence, the optimisation problem must be decomposed into subproblems that can be evaluated separately. To optimise the subproblems, the proposed algorithm allows using embedded deterministic algorithms, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to expedite the optimisation. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on an automated sheet metal press line from the automotive industry. The objective is to find control parameters that maximise the line’s production rate. The results show that the proposed algorithm manages to find optimal control parameters efficiently within the practical timeframe. This is a step forward in press line optimisation since to the authors’ knowledge this is the first time a press line has been optimised efficiently in this way.
Increased production rate and robot motion smoothness in a sheet metal press line are essential. Smooth robot motions avoid unplanned production interruptions and excessive wear of the robots. Reaching high production rate and smooth motions requires tuning of the tending press robot control to minimise the cycle time and jerk. Doing this for a press line with multiple robots is a complex large-scale problem. To model such problems for the optimisation process, computer simulations become almost essential. This work presents simulation-based optimisation of the time and jerk of robotic press tending operations and investigates the importance of including the robot motion’s smoothness. An optimiser works in concert with a simulation model of a sheet metal press line and its parametrised control system. The effect of including jerk minimisation in the objective function is tested on a real-world problem concerning a sheetmetal press line. The results illustrate the importance of including jerk-minimisation as an objective in the optimisation.Furthermore, the performance of this approach is compared with manual tuning by experienced operators. The results show that the proposed simulation-based optimisation approach outperforms manual tuning.
Today most industries aim at reducing energy consumption to become sustainable and environment-friendly. The automotive industry, with mass production and large volumes, is one such example. With many robots working round the clock, there is great potential to save energy. In this climate there is a need for robot simulation models that can be used for motion and task execution optimisation and which are aimed lowering energy consumption. This paper presents an energy consumption model for 2D-belt robots for press line tending in the automotive sector. The energy model is generic for 2D-belt robots and is entirely based on component specifications (e.g., dimensions, masses, inertia). An implementation and validation against a real 2D-belt tending robot used in the automotive industry is performed and presented. The purpose and usefulness of the energy model is also demonstrated by two application cases; the investigation of potential energy reductions achieved by reducing the weight of gripper tools, and by using mechanical brakes when the robot is idle.
Computer Aided Process Planning has received more attention recently due to considerable progress in the aspects of both technology and theory. Beside the traditional trends and efforts to integrate the product design and process planning activities usually referred to as concurrent engineering, virtual manufacturing tools have opened new horizons to this domain. This paper describes how to combine an existing modular fixture design with process planning and simulation tools. The proposed concurrent architecture consists of a functional model and an operational workflow for the design of modular fixtures within the process-planning phase. Two different paradigms, the Variant and the Generative, are discussed in relation to the proposed architecture. Fixtures for Body in White lines are a crucial design problem in the automotive industry. Therefore, the proposed architecture has been tested and investigated in such an environment.
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.
This article presents a systematic literature review on the Plug and Produce concept in advanced automated manufacturing control systems. Over recent decades, this concept has evolved significantly, with researchers focusing on enhancing its applicability and improving its conceptual, logical, and physical aspects across various sub-areas such as system design, methodologies, and supporting tools within the Industry 4.0 and Industry 5.0 frameworks. The review offers technical insights on the research domain of Plug and Produce accompanied by an analytical schematic outlining five key evolving research streams ranging from system design framework, and functionality features, up to the empirical application. Additionally, the article discusses important issues surrounding the evolution of Plug and Produce in alignment with emerging trends within Industry 5.0 automation. By analyzing the literature and current trends in industrial automation, the article highlights critical key development directions for shaping the future of manufacturing systems focusing on smart, circular, and human-centric solutions using Plug and Produce. ©
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.
Plug & Produce is a modern automation concept in smart manufacturing for modular, quick, and easy reconfigurable production. The system’s flexibility allows for the configuration of production with abstraction, meaning that the production resources participating in a specific production plan are only known in the online phase. The safety assurance process of such a system is complex and challenging. This work aims to assist the safety assurance when utilizing a highly flexible Plug & Produce concept that accepts instant logical and physical reconfiguration. In this work, we propose a concept for online hazard identification of Plug & Produce systems, the proposed concept, allows for the detection of hazards in the online phase and assists the safety assurance as it provides the hazard list of all possible executable alternatives of the abstract goals automatically. Further, it combines the safety-related information with the control logic allowing for safe planning of operations. The concept was validated with a manufacturing scenario that demonstrates the effectiveness of the proposed concept.
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.
This paper presents a model-based safety software that performs Hazard Identification (HI) for Plug&Produce (P&P) systems automatically. P&P systems, inspired by Plug&Play in computers, aim to integrate devices and tools into the manufacturing system with minimum integration efforts and costs. When plugging a new resource, it will exchange all the required information with the manufacturing system and be ready to operate within minutes rather than days or weeks. One of the challenges that face this concept is performing proper risk assessment after each change in the system. Therefore, the risk assessment needs to be automated as much as possible. This paper is about automating one risk assessment step: Hazard Identification. A new safety model is designed to identify hazards. The presented software analyses this model by implementing a novel algorithm that uses lookup tables to cover various possible hazards when resources work together. This software will support the risk reduction team by drastically reducing the time needed for HI and being ready for the next steps in risk analyses. Automating identifying hazards is an essential step towards automating the entire risk assessment process and achieving safe P&P systems.
Geometrical collision detection is a time and resource consuming simulation task. In order to decrease time and resources, a general method applicable for 2D motions has been developed. The method is useful in simulation cases where 3D CAD data is part of an iterative method, e.g. optimization. The method is based on a transformation of a general 3D problem to a 2D problem, eliminating the need of 3D CAD models. Press Line simulations during the last decade have been accepted as a quality improvement method. Today simulations of automated press lines are done for internal collision checks in dies and external collision checks against dies and material handling equipment. If these collisions are not detected in simulations, they result in delays, in introduction of a new product in the line, so called line tryout or later when the line is ramped up to decide rate. The results of these collisions are used for pre-die design, design of grippers, maintenance and production planning. In this paper a new method, based on 2D simplifications, is developed and tested successfully in a virtual model of a press line at Volvo Car Manufacturing. Die Uppers 2 917 708 triangles and Die Lowers 602 686 triangles where reduced to 58 and 90 points. The result of the method shows substantial reduction of geometry data and considerable improvement in collision detection evaluation time over general 3D algorithms in the tested case.
Geometrical collision detection is a time and resource consuming simulation task. In order to decrease time and resources, a general method has been developed. The method is useful in simulation cases where 3D CAD data is part of an iterative method, e.g. optimization. The method is based on a transformation of a general 3D problem to a 2D problem, eliminating the need of 3D CAD models. Press Line simulations during the last decade have been accepted as a quality improvement method. Today simulations of automated press lines are done for internal collision checks in dies and external collision checks against dies and material handling equipment. If these collisions are not detected in simulations, they result in delays, in introduction of a new product in the line, so called line tryout or later on when the line is ramped up to decided rate. The results of these collisions are used for pre-die design, design of grippers, maintenance and production planning. In this paper a new method, based on 2D simplifications, is developed and tested successfully in a virtual model of a press line at Volvo Car Manufacturing. Die Uppers 2 917 708 triangles and Die Lowers 602 686 triangles where reduced to 58 and 90 points. The result of the method shows substantial reduction of geometry data and a considerable improvement in collision detection evaluation time over general 3D algorithms in the tested case.
In order to perform efficient geometrical simulation and virtual commissioning in stamping, three fields are investigated namely: simulation building time, collision detection time and optimization time. Hence, reducing time is the main theme of this paper. To reduce simulation building time and optimization time, an efficient stamping simulation model is built and tested. Collision detection time is examined by a relative motion method based on 3D to 2D geometrical collision detection. The presented results mean that simulation and virtual commissioning can be performed at least ten times faster compared to standard approaches.
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.
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 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.