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Zhang, Xiaoxiao
Publications (6 of 6) Show all publications
Massouh, B., Danielsson, F., Ramasamy, S., Khabbazi, M. R. & Zhang, X. (2024). Online Hazard Detection in Reconfigurable Plug & Produce Systems. In: Silva, F.J.G., Pereira, A.B., Campilho, R.D.S.G. (Ed.), Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.: FAIM 2023. Paper presented at International Conference on Flexible Automation and Intelligent Manufacturing FAIM 2023, 18-22 June, Porto, Portugal (pp. 889-897). Springer Nature
Open this publication in new window or tab >>Online Hazard Detection in Reconfigurable Plug & Produce Systems
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2024 (English)In: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.: FAIM 2023 / [ed] Silva, F.J.G., Pereira, A.B., Campilho, R.D.S.G., Springer Nature, 2024, p. 889-897Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2024
Series
Lecture Notes in Mechanical Engineering
Keywords
Plug & Produce, reconfigurable manufacturing, safety assessment, hazard identification
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology; Production Technology
Identifiers
urn:nbn:se:hv:diva-20884 (URN)10.1007/978-3-031-38241-3_97 (DOI)2-s2.0-85171556008 (Scopus ID)9783031382406 (ISBN)9783031382413 (ISBN)
Conference
International Conference on Flexible Automation and Intelligent Manufacturing FAIM 2023, 18-22 June, Porto, Portugal
Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2024-01-30
Ramasamy, S., Bennulf, M., Zhang, X., Hammar, S. & Danielsson, F. (2023). Online Path Planning in a Multi-agent-Controlled Manufacturing System. Paper presented at 31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, Detroit, 19 June 2022, through 23 June 2022 Code 285199. Lecture Notes in Mechanical Engineering, 124-134
Open this publication in new window or tab >>Online Path Planning in a Multi-agent-Controlled Manufacturing System
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2023 (English)In: Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364, p. 124-134Article in journal (Refereed) Published
Abstract [en]

In recent years the manufacturing sectors are migrating from mass production to mass customization. To be able to achieve mass customization, manufacturing systems are expected to be more flexible to accommodate the different customizations. The industries which are using the traditional and dedicated manufacturing systems are expensive to realize this transition. One promising approach to achieve flexibility in their production is called Plug & Produce concept which can be realized using multi-agent-based controllers. In multi-agent systems, parts and resources are usually distributed logically, and they communicate with each other and act as autonomous agents to achieve the manufacturing goals. During the manufacturing process, an agent representing a robot can request a path for transportation from one location to another location. To address this transportation facility, this paper presents the result of a futuristic approach for an online path planning algorithm directly implemented as an agent in a multi-agent system. Here, the agent systems can generate collision-free paths automatically and autonomously. The parts and resources can be configured with a multi-agent system in the manufacturing process with minimal human intervention and production downtime, thereby achieving the customization and flexibility in the production process needed. 

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Autonomous agents; Computer aided manufacturing; Motion planning; Online systems; Customisation; Manufacturing process; Manufacturing sector; Mass customization; Mass production; Multi agent; On-line path planning; Path planner service; Path planners; Plug & produce; Multi agent systems
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Technology
Identifiers
urn:nbn:se:hv:diva-19432 (URN)10.1007/978-3-031-18326-3_13 (DOI)2-s2.0-85141873498 (Scopus ID)
Conference
31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, Detroit, 19 June 2022, through 23 June 2022 Code 285199
Funder
Knowledge Foundation, 20200036
Note

CC-BY 4.0

The work was funded by PoPCoRN project by KK-stiftelsen, Sweden.

31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022; Conference date: 19 June 2022 through 23 June 2022; Conference code: 285199

Available from: 2023-09-12 Created: 2023-09-12 Last updated: 2024-01-18Bibliographically approved
Eriksson, K. M., Ramasamy, S., Zhang, X., Wang, Z. & Danielsson, F. (2022). Conceptual framework of scheduling applying discrete event simulation as an environment for deep reinforcement learning. Paper presented at 55th CIRP Conference on Manufacturing Systems. Procedia CIRP, 107, 955-960
Open this publication in new window or tab >>Conceptual framework of scheduling applying discrete event simulation as an environment for deep reinforcement learning
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2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 107, p. 955-960Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Reinforcement learning; Discrete event simulation; Energy optimal scheduling; Inverse scheduling; Industty 4.0
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Work Integrated Learning; Production Technology
Identifiers
urn:nbn:se:hv:diva-18474 (URN)10.1016/j.procir.2022.05.091 (DOI)2-s2.0-85132264077 (Scopus ID)
Conference
55th CIRP Conference on Manufacturing Systems
Note

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. 

Available from: 2022-06-13 Created: 2022-06-13 Last updated: 2024-04-12
Ramasamy, S., Zhang, X., Bennulf, M. & Danielsson, F. (2019). Automated Path Planning for Plug Produce in a Cutting-tool Changing Application. In: 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): . Paper presented at 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 356-362). , Article ID 8869398.
Open this publication in new window or tab >>Automated Path Planning for Plug Produce in a Cutting-tool Changing Application
2019 (English)In: 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2019, p. 356-362, article id 8869398Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a path planning algorithm is designed and tested with a real robot for a Plug & Produce demonstrator. The demonstrator is divided into modules that can be connected and removed. Modules are used for various processes like tool change and storage. This paper focuses on the process of cutting-tool change for the production industry. The Plug & Produce demonstrator uses a multi-agent system where parts and resources are agents. A part agent, e.g., a cutting-tool, can request a robot to perform skills like transportation. This requires the robot to be autonomous. The aim of this paper is to automate the path planning for industrial robotics in a Plug & Produce system. This is done by implementing a sampling based RRT algorithm combined with a collision detection function in RobotStudio. With various real time scenarios, the path planning execution time is observed and presented in the paper.

Keywords
Collision avoidance, Industry4.0, Multi-agent systems, Path planning, Plug & Produce, Plugs, rapidly-exploring random tree, Robot kinematics, Service robots, Tools
National Category
Robotics Control Engineering Production Engineering, Human Work Science and Ergonomics
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
urn:nbn:se:hv:diva-14681 (URN)10.1109/ETFA.2019.8869398 (DOI)2-s2.0-85074208961 (Scopus ID)
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2020-01-14Bibliographically approved
Ericsson, M., Zhang, X. & Christiansson, A.-K. (2018). Virtual Commissioning of Machine Vision Applications in Aero Engine Manufacturing. In: Proceedings of The 15th International Conference on Control,Automation, Robotics and Vision, November 18-21, 2018: . Paper presented at 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Marina Bay Sands Expo and Convention Centre, Singapore, November 18-21, 2018 (pp. 1947-1952). , Article ID 0293.
Open this publication in new window or tab >>Virtual Commissioning of Machine Vision Applications in Aero Engine Manufacturing
2018 (English)In: Proceedings of The 15th International Conference on Control,Automation, Robotics and Vision, November 18-21, 2018, 2018, p. 1947-1952, article id 0293Conference paper, Published paper (Refereed)
Abstract [en]

New aero engine design puts new demands on the manufacturing methods with increased automation level. Therefore, the use of vision sensors for control and guiding of industrial robots is being increasingly used. In such system, it is need to customise the machine vision system with real components in the real environment which is normally done close to the start-up of the production. This paper addresses a new concept for designing, programming, analysing, testing and verifying a machine vision application early in the design phase, called Virtual Machine Vision. It is based on a robot simulation software where the real machine vision application is simulated before the implementation in the production line. To verify the Virtual Machine Vision concept an advanced stereo vision application was used. Using two captured images from the robot simulated environment, camera calibration, image analysis and stereo vision algorithms are applied to determine a desired welding joint. The information of the weld joint, i.e. robot position and orientation for the weld path, are sent from the machine vision system to the robot control system in the simulation environment and the weld path is updated. The validation of the Virtual Machine Vision concept using the stereo vision application is promising for industrial use, and it is emphasised that the same programs are used in the virtual and real word.

Keywords
Vision for robots, Image-based modeling, Modeling and identification
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology; ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-13390 (URN)10.1109/ICARCV.2018.8581207 (DOI)000459847700325 ()2-s2.0-85060823404 (Scopus ID)978-1-5386-9581-4 (ISBN)
Conference
2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Marina Bay Sands Expo and Convention Centre, Singapore, November 18-21, 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2020-02-05Bibliographically approved
Bolmsjö, G., Bennulf, M. & Zhang, X. (2016). Safety System for Industrial Robots to Support Collaboration. In: Christopher Schlick, Stefan Trzcieliński (Ed.), Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Proceedings of the AHFE 2016 International Conference on Human Aspects of Advanced Manufacturing, July 27-31, 2016, Walt Disney World®, Florida, USA (pp. 253-265). Springer International Publishing
Open this publication in new window or tab >>Safety System for Industrial Robots to Support Collaboration
2016 (English)In: Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Proceedings of the AHFE 2016 International Conference on Human Aspects of Advanced Manufacturing, July 27-31, 2016, Walt Disney World®, Florida, USA / [ed] Christopher Schlick, Stefan Trzcieliński, Springer International Publishing , 2016, p. 253-265Chapter in book (Refereed)
Abstract [en]

The ongoing trend towards manufacturing of customized products generates an increased demand on highly efficient work methods to manage product variants through flexible automation. Adopting robots for automation is not always feasible in low batch production. However, the combination of humans together with robots performing tasks in collaboration provides a complementary mix of skill and creativity of humans, and precision and strength of robots which support flexible production in small series down to one-off production. Through this, collaboration can be used with implications on reconfiguration and production. In this paper, the focus and study is on designing safety for efficient collaboration operator—robot in selected work task scenarios. The recently published ISO/TS 15066:2016 describing collaboration between operator and robot is in this context an important document for development and implementation of robotic systems designed for collaboration between operator and robot.

Place, publisher, year, edition, pages
Springer International Publishing, 2016
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 490
Keywords
Human-robot interaction, Collaboration, Robot safety
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
Production Technology; ENGINEERING, Manufacturing and materials engineering
Identifiers
urn:nbn:se:hv:diva-10160 (URN)10.1007/978-3-319-41697-7_23 (DOI)2-s2.0-84986272619 (Scopus ID)978-3-319-41696-0 (ISBN)978-3-319-41697-7 (ISBN)
Available from: 2016-11-21 Created: 2016-11-21 Last updated: 2020-01-17Bibliographically approved
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