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Liu, H. & Wang, L. (2020). Remote human–robot collaboration: A cyber–physical system application for hazard manufacturing environment. Journal of manufacturing systems, 54, 24-34
Open this publication in new window or tab >>Remote human–robot collaboration: A cyber–physical system application for hazard manufacturing environment
2020 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 54, p. 24-34Article in journal (Refereed) Published
Abstract [en]

Collaborative robot's lead-through is a key feature towards human–robot collaborative manufacturing. The lead-through feature can release human operators from debugging complex robot control codes. In a hazard manufacturing environment, human operators are not allowed to enter, but the lead-through feature is still desired in many circumstances. To target the problem, the authors introduce a remote human–robot collaboration system that follows the concept of cyber–physical systems. The introduced system can flexibly work in four different modes according to different scenarios. With the utilisation of a collaborative robot and an industrial robot, a remote robot control system and a model-driven display system is designed. The designed system is also implemented and tested in different scenarios. The final analysis indicates a great potential to adopt the developed system in hazard manufacturing environment.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Cyber–physical systems, Human–robot collaboration, Manufacturing
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-267862 (URN)10.1016/j.jmsy.2019.11.001 (DOI)2-s2.0-85075565506 (Scopus ID)
Note

QC 20200219

Available from: 2020-02-19 Created: 2020-02-19 Last updated: 2020-02-19Bibliographically approved
Pérez, L., Rodríguez-Jiménez, S., Rodríguez, N., Usamentiaga, R., García, D. F. & Wang, L. (2020). Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry. The International Journal of Advanced Manufacturing Technology, 106(3-4), 851-863
Open this publication in new window or tab >>Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry
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2020 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 106, no 3-4, p. 851-863Article in journal (Refereed) Published
Abstract [en]

Robots are perfect substitutes for skilled workforce on some repeatable, general, and strategically important tasks, but this substitution is not always feasible. Despite the evolution of robotics, some industries have been traditionally robot-reluctant because their processes involve large or specific parts and non-serialized products; thus, standard robotic solutions are not cost-effective. This work presents a novel approach for advanced manufacturing applied to the aerospace industry, combining the power and the repeatability of the robots with the flexibility of humans. The proposed approach is based on immersive and symbiotic collaboration between human workers and robots, presenting a safe, dynamic, and cost-effective solution for this traditionally manual and robot-reluctant industry. The proposed system architecture includes control, safety, and interface components for the new collaborative manufacturing process. It has been validated in a real-life case study that provides a solution for the manufacturing of aircraft ribs. The results show that humans and robots can share the working area simultaneously without physical separation safely, providing beneficial symbiotic collaboration and reducing times, risks, and costs significantly compared with manual operations.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Aerospace manufacturing, Human–robot collaboration, Industry 4.0, Productivity, Robots, Safety, Accident prevention, Aerospace industry, Aircraft manufacture, Cost effectiveness, Costs, Robotics, Advanced manufacturing, Collaborative approach, Collaborative manufacturing, Cost-effective solutions, Increased productivity, Physical separation, System architectures
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-267966 (URN)10.1007/s00170-019-04638-6 (DOI)000499425600002 ()2-s2.0-85075905258 (Scopus ID)
Note

QC 20200329

Available from: 2020-03-29 Created: 2020-03-29 Last updated: 2020-03-29Bibliographically approved
Wang, Y. & Wang, L. (2020). Whole-body collision avoidance control design using quadratic programming with strict and soft task priorities. Robotics and Computer-Integrated Manufacturing, 62, Article ID 101882.
Open this publication in new window or tab >>Whole-body collision avoidance control design using quadratic programming with strict and soft task priorities
2020 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 62, article id 101882Article in journal (Refereed) Published
Abstract [en]

Despite safe mechanical design is necessary for the collaborative robots, we can not underestimate the importance of active safety due to a multi-objective control design. Active safety not only complements the mechanical compliance but also enables classical industrial robots the ability to fulfill additional task-space objectives. Using the gradient of the collision avoidance task as hard constraints of a quadratic programming (QP) controller, we assign strict priority to avoid collisions and specify other QP controller objectives with soft task priorities. Through experiments performed on a dual-arm robot, we show that the proposed solution is able to generate safe robot motion that fulfills the task specifications while keeping the feasibility of the underlying quadratic optimization problem.

Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-266171 (URN)10.1016/j.rcim.2019.101882 (DOI)000501405400006 ()2-s2.0-85073568052 (Scopus ID)
Note

QC 20200113

Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-01-13Bibliographically approved
Ji, W., Yin, S. & Wang, L. (2019). A big data analytics based machining optimisation approach. Journal of Intelligent Manufacturing, 30(3), 1483-1495
Open this publication in new window or tab >>A big data analytics based machining optimisation approach
2019 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, Vol. 30, no 3, p. 1483-1495Article in journal (Refereed) Published
Abstract [en]

Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

Place, publisher, year, edition, pages
SPRINGER, 2019
Keywords
Big data analytics, Machining optimisation, Hybrid algorithm, Deep belief network, Genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-246246 (URN)10.1007/s10845-018-1440-9 (DOI)000459423700032 ()2-s2.0-85050695013 (Scopus ID)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-06-11Bibliographically approved
Yin, S., Ji, W. & Wang, L. (2019). A machine learning based energy efficient trajectory planning approach for industrial robots. In: Procedia CIRP: . Paper presented at 52nd CIRP Conference on Manufacturing Systems, CMS 2019, 12 June 2019 through 14 June 2019 (pp. 429-434). Elsevier B.V.
Open this publication in new window or tab >>A machine learning based energy efficient trajectory planning approach for industrial robots
2019 (English)In: Procedia CIRP, Elsevier B.V. , 2019, p. 429-434Conference paper, Published paper (Refereed)
Abstract [en]

Towards an energy efficient trajectory planning of industrial robot (IR), this paper proposes a machine learning based approach. Within the context, the IR’s movements are digitalised in joint space first, which allows using data attributes to represent IR’s trajectories. Moreover, a set of designed trajectories which can address IRs workspace are followed by the IR, and meanwhile, the energy consumption is measured. Then data sets are generated by combining the trajectory data and measured energy consumption data, and they are used to train a machine learning model. On top of that, the trained model provides a fitness function to evolution based or swarm-intelligence based algorithms to obtain a near-optimal or optimal trajectory. Finally, a simplified case study is demonstrated to validate the proposed method. The method provides a direct connection between joint control and energy efficiency objective, by which the solution space can be obviously relaxed, compared to the existing methods.

Place, publisher, year, edition, pages
Elsevier B.V., 2019
Keywords
Energy efficiency, Industrial robot, Machine learning, Trajectory planning
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-262479 (URN)10.1016/j.procir.2019.03.074 (DOI)2-s2.0-85068434469 (Scopus ID)
Conference
52nd CIRP Conference on Manufacturing Systems, CMS 2019, 12 June 2019 through 14 June 2019
Note

QC 20191016

Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16Bibliographically approved
Liu, Y.-K. -., Zhang, X.-S. -., Zhang, L., Tao, F. & Wang, L. (2019). A multi-agent architecture for scheduling in platform-based smart manufacturing systems. Frontiers of Information Technology and Electronic Engineering, 20(11), 1465-1492
Open this publication in new window or tab >>A multi-agent architecture for scheduling in platform-based smart manufacturing systems
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2019 (English)In: Frontiers of Information Technology and Electronic Engineering, ISSN 2095-9184, Vol. 20, no 11, p. 1465-1492Article in journal (Refereed) Published
Abstract [en]

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

Place, publisher, year, edition, pages
Zhejiang University Press, 2019
Keywords
Multi-agent, Platform, Scheduling, Smart manufacturing, TP27
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-267893 (URN)10.1631/FITEE.1900094 (DOI)000511703300002 ()2-s2.0-85076489101 (Scopus ID)
Note

QC 20200218

Available from: 2020-02-18 Created: 2020-02-18 Last updated: 2020-03-11Bibliographically approved
Yue, C., Gao, H., Liu, X., Liang, S. Y. & Wang, L. (2019). A review of chatter vibration research in milling. CHINESE JOURNAL OF AERONAUTICS, 32(2), 215-242
Open this publication in new window or tab >>A review of chatter vibration research in milling
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2019 (English)In: CHINESE JOURNAL OF AERONAUTICS, ISSN 1000-9361, Vol. 32, no 2, p. 215-242Article, review/survey (Refereed) Published
Abstract [en]

Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of research has been dedicated to the development of technologies that are able to predict and detect chatter. The purpose of these technologies is to facilitate the avoidance of chatter during cutting processes, which leads to better surface precision, higher productivity, and longer tool life. This paper summarizes the current state of the art in research regarding the problems of how to arrive at stable chatter prediction, chatter identification, and chatter control/suppression, with a focus on milling processes. Particular focus is placed on the theoretical relationship between cutting chatter and process damping, tool runout, and gyroscopic effect, as well as the importance of this for chatter prediction. The paper concludes with some reflections regarding possible directions for future research in this field. 2019 Chinese Society of Aeronautics and Astronautics.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2019
Keywords
Chatter, Gyroscopic effects, Milling, Process damping, Tool runout, LIO T, 1992, JOURNAL OF ENGINEERING FOR INDUSTRY-TRANSACTIONS OF THE ASME, V114, P146 shid Amir, 2006, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, V46, P1626 soff Ahmad R., 2011, TRENDS IN AEROSPACE MANUFACTURING 2009 INTERNATIONAL CONFERENCEInternational Conference on Trends in Aerospace Manufacturing (TRAM), SEP 09-10, 2009, Sheffield, ENGLAND, V26, lachandran B, 2000, MECCANICA, V35, P89 urc Etienne, 2011, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, V51, P928 ng JJJ, 2002, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, V42, P695 guy Sebastien, 2011, MACHINING SCIENCE AND TECHNOLOGY, V15, P153
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-246271 (URN)10.1016/j.cja.2018.11.007 (DOI)000459794000001 ()2-s2.0-85060355021 (Scopus ID)
Note

QC 20190326

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-04Bibliographically approved
Liu, S., Zhang, Y., Liu, Y., Wang, L. & Wang, X. V. (2019). An 'Internet of Things' enabled dynamic optimization method for smart vehicles and logistics tasks. Journal of Cleaner Production, 215, 806-820
Open this publication in new window or tab >>An 'Internet of Things' enabled dynamic optimization method for smart vehicles and logistics tasks
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2019 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 215, p. 806-820Article in journal (Refereed) Published
Abstract [en]

Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an 'Internet of Things'-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles' utilization rate, and achieving real-time logistics services with high efficiency. reserved.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Internet of things, Green logistics, Dynamic optimization, Real-time information
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-245887 (URN)10.1016/j.jclepro.2018.12.254 (DOI)000459358300068 ()2-s2.0-85060923837 (Scopus ID)
Note

QC 20190308

Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-03-08Bibliographically approved
Liu, Y., Wang, L., Wang, X. V., Xu, X. & Jiang, P. (2019). Cloud manufacturing: key issues and future perspectives. International journal of computer integrated manufacturing (Print)
Open this publication in new window or tab >>Cloud manufacturing: key issues and future perspectives
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2019 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed) Epub ahead of print
Abstract [en]

Since the introduction of the concept of cloud manufacturing in 2010, research on it has been ongoing for more than eight years, and much progress has been made. However, existing research indicates that people lack common and comprehensive understandings of some of the key issues with cloud manufacturing such as the concept, operation model, service mode, technology system, architecture, and essential characteristics. Moreover, few studies discuss in depth the relationships between cloud manufacturing and some closely related concepts such as cloud computing-based manufacturing, Cyber-Physical Systems (CPS), smart manufacturing, Industry 4.0, and Industrial Internet. Knowledge as a core supporting factor in cloud manufacturing has rarely been discussed systematically. Also, so far there has been no standardised definition for cloud manufacturing yet. All these are key issues to be further discussed and analysed in cloud manufacturing. In order to clarify the issues above and provide reference for future research and implementation, this paper conducts a comprehensive, systematic, and in-depth discussion and analysis of the aforementioned issues in cloud manufacturing and presents an alternative definition for cloud manufacturing based on the analysis of 12 existing definitions. Future perspectives of cloud manufacturing are also discussed with respect to both academic research and industrial implementation.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
Cloud manufacturing, cloud computing, smart manufacturing, Cyber-Physical Systems (CPS), Industry 4, 0, Industrial Internet
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-255563 (URN)10.1080/0951192X.2019.1639217 (DOI)000475054500001 ()2-s2.0-85068750206 (Scopus ID)
Note

QC 20190808

Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2020-03-09Bibliographically approved
Wang, L., Meng, Y. & Ji, W. (2019). Cutting energy consumption modelling for prismatic machining features. The International Journal of Advanced Manufacturing Technology, 103(5-8), 1657-1667
Open this publication in new window or tab >>Cutting energy consumption modelling for prismatic machining features
2019 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 103, no 5-8, p. 1657-1667Article in journal (Refereed) Published
Abstract [en]

Targeting energy-efficient machining process planning, this paper presents a follow-up research on cutting energy consumption modelling for prismatic machining features (PMFs). Based on the investigation of plastic deformation-based energy consumption, its energy consumption model is extended to PMFs by refining machining time and feed at corners. Material removal volume associated with machining strategies for the PMF machining is considered as well. Moreover, cutting energy consumption models are established for the selected PMFs, i.e. face, step, slot and pocket. Finally, energy consumptions in machining of a designed test part, involving the established models of cutting energy consumption for the selected PMFs, are measured and compared with estimated energy consumptions to validate the developed models.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Cutting energy consumption, Machining, Prismatic machining features
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-255732 (URN)10.1007/s00170-019-03667-5 (DOI)000476625500002 ()2-s2.0-85067792776 (Scopus ID)
Note

QC 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8679-8049

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