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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Reichsthaler, Luisa Toth, Daniel Szaller, Ádám Sihn, Wilfried Fraunhofer Austria Research GmbH Division of Factory Planning and Production Management Vienna1040 Austria Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest1111 Hungary TU Wien Institute of Management Science Vienna1040 Austria

In recent years, manufacturing companies have been increasingly confronted with rising demands relating to their production, uncertain conditions in their environment and volatile markets. To fulfil the diversified customers' demands and rapidly changing markets, manufacturing companies need to achieve and maintain high productivity and quality. This requires fast responses, sufficient flexibility and short lead times. Therefore, flexible, resilient and customer-oriented production processes are becoming more and more important. Current optimization measures of industrial environments focus primarily on increasing efficiency while reducing costs. The reliance on other factors, such as a resilient as well as economically, ecologically and socially sustainable manufacturing system, has not been sufficiently taken into account yet. Bridging the gap between research and practice, this paper proposes a holistic approach that assesses resilience, sustainability and efficiency measures of manufacturing companies. A multi-criteria assessment model is developed to stabilize qualitative and quantitative measures within these dimensions. Furthermore, the model is tested in an Austrian small and medium-sized (SME) enterprise in the wood processing industry. The targeted groups are SMEs and multinational companies. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

关键词： assessment model efficiency production optimization resilience sustainability

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Learning Factories towards Industry 5.0: Evolutionary or Revolutionary? 19

Learning Factories towards Industry 5.0: Evolutionary or Rev...

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19th IMEKO TC10 Conference on MACRO meets NANO in Measurement for Diagnostics, Optimization and control

作者： Gyulai, Tamás Wolf, Péter Kása, Ferenc Viharos, Zsolt János IQ Kecskemét Industrial Research Kecskemét Hungary Széchenyi István University Győr Hungary Pest County Municipality Budapest Hungary Neumann Nonprofit Ltd. Budapest Hungary Centre of Excellence in Production Informatics and Control Centre of Excellence of the Hungarian Academy of Sciences Budapest Hungary John von Neumann University Kecskemét Hungary

ISBN: (纸本)9781713884125

Rather than representing a technological leap forward, Industry 5.0 actually nests the Industry 4.0 approach in a broader context, providing regenerative purpose and directionality to the technological transformation of industrial production for people-planet-prosperity [1]. Consequently, Industry 5.0 can be considered as the new engine of the economic and societal transition with a societal concept which can mean more distributed well-being with human-centric and sustainable, resilient industry. The advantage of the learning factory concept therefore lies in the combination of the realistic factory environment, processes and transparency of the structured activities which can provide testing of new features, modules, functionalities, tools, and technologies based on the existing Industry 4.0 framework. Especially, the concepts of implementing new business models with benchmarking emphasise the major difference in achievable results. Transition to a circular economy can only be achieved if up-skilling and re-skilling of workers can also be done which is the core function of the learning factory. © 2023 19th IMEKO TC10 Conference "MACRO meets NANO in Measurement for Diagnostics, Optimization and control".

关键词： Industry 5.0 Learning Factory production Responsible Consumption Sustainable and Resilient Industry

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A multi-criteria approach for assessing resilience, sustainability and efficiency measures in manufacturing companies

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Procedia CIRP 2023年 120卷 547-552页

作者： Luisa Reichsthaler Daniel Toth Ádám Szaller Wilfried Sihn Fraunhofer Austria Research GmbH Division of Factory Planning and Production Management Vienna 1040 Austria Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest 1111 Hungary TU Wien Institute of Management Science Vienna 1040 Austria

关键词： production optimization resilience sustainability efficiency assessment model

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Novel heuristic approach to integrating task sequencing and production system configuration 55

Novel heuristic approach to integrating task sequencing and ...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Tsutsumi, Daisuke Kovács, András Szalóki, Ádám Hitachi Ltd. Research and Development Group 292 Yoshida-chyo Totsuka-ku Yokohama-shi Kanagawa244-0817 Japan EPIC Centre of Excellence in Production Informatics and Control SZTAKI Kende 13-17 BudapestH-1111 Hungary

The robot system integration (SI) business targets the construction of production lines for various products that differ from customer to customer. Improving the efficiency of line design is important for increasing profits in the SI business. Especially in the construction of assembly lines, the optimal system configuration, with an appropriate combination of robotic and human stations, is critical for maximizing the return on investment of customers. Yet, the optimal system configuration depends heavily on the task sequence in the process plan. In this paper, task sequencing and system configuration are regarded as two interdependent sub-problems of line design. The exact joint optimum of the two problems can be found only if they are solved together, in an integrated way. However, this is computationally intractable for problems of industrially relevant sizes. To overcome this challenge, a heuristic method exploiting industrial knowledge is proposed for solving the two problems simultaneously. The heuristic constructs promising task sequences according to the capabilities of the applicable resources, considering aspects such as, among others, human work quality or robot tool change times. Then, a close-to-optimal system configuration is computed by considering the promising task sequences and their various relevant combinations. The proposed method is illustrated in a case study on the assembly of electrical components in the automotive industry. © 2022 The Authors. Published by Elsevier B.V.

关键词： assembly lines optimization system configuration task sequencing

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Digital twin assisted workpiece referencing for compensating the stock deviation of casted parts

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Procedia CIRP 2023年 118卷 288-293页

作者： Tamás Cserteg Gábor Erdős Gergely Horváth András Kovács EPIC Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Budapest H-1111 Hungary Faculty of Informatics Eötvös Loránd University Budapest H-1117 Hungary Department of Manufacturing Science and Technology Budapest University of Technology and Economics Budapest H-1111 Hungary

Stock deviation of casted parts needs to be handled by adapting the CNC machining code to every new batch. Usually, human experts deal with this workpiece referencing task, but the demand for automation is expressed by the industry. This paper introduces a Digital Twin (DT) supported workpiece referencing method, implemented in the following steps: building the DT of the CNC machining cell, loading measurements of the casted part into the DT, solving the workpiece referencing problem as a convex optimization problem, and generating the compensated CNC code. The proposed approach is illustrated in a case study from the automotive industry.

关键词： Machining Workpiece referencing Digital Twin Convex optimization

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Development of a novel visual servoing probe test method for fault diagnosis of printed circuit boards

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IFAC-PapersOnLine 2023年第2期56卷 5248-5254页

作者： B. Tipary Á. Juniki F.G. Erdős E. Takács K. Németh Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary Department of Manufacturing Science and Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Budapest Hungary University of Pannonia Veszprém Hungary

Printed circuit board (PCB) measurement and repair is a challenging task that requires experience and expertise to perform. PCB diagnosis and repair shops employ skilled operators to carry out the corresponding measurement tasks using measuring instruments (e.g., oscilloscopes, multimeters) in order to uncover the condition of a particular product. However, these tasks are often repetitive and meticulous, and additionally, the results need to be collected and carefully documented so that the gathered experience regarding the product can be re-used when the next product of the same type arrives into the shop. Nevertheless, the diagnosis of used PCBs is less researched and current flexible automation possibilities are limited. In this paper, a novel visual servoing probe test method and measurement tool are proposed to provide a flexible solution for PCB diagnosis with a higher level of automation. The aim of the approach is to reduce the burden on the operators by carrying out the repetitive measurement tasks and automatically storing the results while leaving the responsibility of measurement profile setup to the human expert. The proposed visual servo system uses manually teached-in measurement points, where template patterns are recorded using cameras, and it is capable of compensating positioning errors in the range of a couple of millimeters. The proof of concept of the proposed method is presented through motherboard measuring experiments, with a 99.7% success rate.

关键词： Flexible and reconfigurable manufacturing systems Quality assurance and maintenance Visual servo control Printed circuit board measurement Fault diagnosis

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

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IFAC-PapersOnLine 2023年第2期56卷 4896-4903页

作者： Szilárd H. Tóth Ádám Bárdos Zsolt J. Viharos Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering and Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Centre of Excellence in Production Informatics and Control Eötvös Loránd Research Network (ELKH) Centre of Excellence of the Hungarian Academy of Sciences (MTA) Budapest Hungary Department of Management and Business Law Faculty of Economics John von Neumann University Kecskemét Hungary

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

关键词： Motion control Autonomous Vehicles Vehicle Dynamics control Reinforcement Learning Vehicle Drifting Handling Limits

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Dynamic production Line Re-balancing by Alternative Plans for Compensating Equipment Failures 56

Dynamic Production Line Re-balancing by Alternative Plans fo...

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Tsutsumi, Daisuke Bergmann, Júlia Dobrovoczki, Péter Hayashi, Naohiro Kovács, András Szádoczki, Zsombor Szaller, Ádám Umeda, Shota Hitachi Ltd. Research & Development Group 292 Yoshida-chyo Totsuka-ku Kanagawa Yokohama-shi244-0817 Japan EPIC Centre of Excellence in Production Informatics and Control SZTAKI Budapest Hungary Eötvös Loránd University Doctoral School of Informatics Budapest Hungary Eötvös Loránd University Doctoral School of Mathematics Budapest Hungary Department of Operations Research and Actuarial Sciences Corvinus University of Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Budapest Hungary

As social uncertainty increases, there are changes in values such as safety, sustainability, and response to changes. The manufacturing industry is also required to improve its resilience by dynamically recombining production resources to respond to change. In the operation of a production line, if demand fluctuates due to changes in the external environment or customer needs, or if sudden production fluctuation such as equipment failure occurs, the production line will be stopped due to reconfiguration or equipment restoration. Therefore, the challenge is to continue production and improve productivity even when fluctuation occurs. In order to respond to the above challenges, this research aims to dynamically and quickly re-plan the production line using the existing equipment according to current equipment and production status. In this paper, failures of the production equipment, such as robots and tools, are regarded as production fluctuation factors. In order to continue production with the remaining resources in the event of equipment failure, it is necessary to change the process plan. However, since these are engineering tasks in the production preparation stage, there was the problem of not being able to flexibly change them at the manufacturing site during the production execution stage. In this development, an alternative plan pre-generation and selection method was developed to maintain production by re-allocating tasks during equipment failure. In this approach, first, multiple alternative plans for task allocation for failures in each piece of equipment are planned in advance using a process plan optimization technique. Next, production fluctuations due to equipment failures are detected. Finally, dynamic changes are possible by selecting alternative plans that maximize the throughput of the entire production system. Initial verification results are shown by comparison with the conventional method for equipment failure using simulation on a small-scale

关键词： alternative planning dynamic re-planning production line system configuration task allocation

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Effect of lead time prediction accuracy in trust-based resource sharing

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IFAC-PapersOnLine 2021年第1期54卷 1126-1131页

作者： Ádám Szaller Botond Kádár Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Nowadays, challenges are changing production facilities: decentralized production networks are replacing centralized organizations to remain competitive. This paper investigates a resource sharing approach where matching resource offers and requests are made by an intermediate platform. One of the main pillars of collaboration is to keep promises, especially about deadlines: in the presented model, facilities could rate each other based on trustfulness and choose from offers based on this setting. Lead time prediction accuracy has a direct effect on the real processing intervals: if the prediction was accurate, the deadline could be met, which results in good ratings and a higher possibility to win more jobs. In the paper, effect of lead time prediction accuracy is investigated in trust-based resource sharing, and the performance of facilities is compared with agent-based simulation.

关键词： Co-operative control/manufacturing Multi-agent simulation Manufacturing System Engineering

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