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Wang, Xi Vincent, Dr.ORCID iD iconorcid.org/0000-0001-9694-0483
Alternative names
Biography [eng]

Xi (Vincent) Wang is an Assistant Professor in the Department of Production Engineering, and also the chair of the XPRES research centre's research leading team. He is working with the division of Sustainable Manufacturing. Vincent received his PhD and Bachelor in Mechanical Engineering from the University of Auckland (New Zealand) and Tianjin University (China), respectively in 2013 and 2008. Vincent’s main research focus is on Cloud-based manufacturing, sustainable manufacturing, computer-aided design, process planning, and manufacturing. Additionally, he has been involved with STEP-compliant CNC research (ISO10303/14649) for years.

 

Biography [swe]

Xi (Vincent) Wang is an Assistant Professor in the Department of Production Engineering, and also the chair of the XPRES research centre's research leading team. He is working with the division of Sustainable Manufacturing. Vincent received his PhD and Bachelor in Mechanical Engineering from the University of Auckland (New Zealand) and Tianjin University (China), respectively in 2013 and 2008. Vincent’s main research focus is on Cloud-based manufacturing, sustainable manufacturing, computer-aided design, process planning, and manufacturing. Additionally, he has been involved with STEP-compliant CNC research (ISO10303/14649) for years.

 

Publications (10 of 35) Show all publications
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 ()
Note

QC 20190808

Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved
Zhang, Y., Cheng, Y., Wang, X. V., Zhong, R. Y., Zhang, Y. & Tao, F. (2019). Data-driven smart production line and its common factors. The International Journal of Advanced Manufacturing Technology, 103(1-4), 1211-1223
Open this publication in new window or tab >>Data-driven smart production line and its common factors
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2019 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 103, no 1-4, p. 1211-1223Article in journal (Refereed) Published
Abstract [en]

Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Smart production line (SPL), Common factors, Data-driven, Integration, Energy consumption
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:kth:diva-255570 (URN)10.1007/s00170-019-03469-9 (DOI)000475921300089 ()2-s2.0-85064345033 (Scopus ID)
Note

QC 20190802

Available from: 2019-08-02 Created: 2019-08-02 Last updated: 2019-08-02Bibliographically approved
Wang, X. V. & Wang, L. (2019). Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. International Journal of Production Research, 57(12), 3892-3902
Open this publication in new window or tab >>Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0
2019 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 57, no 12, p. 3892-3902Article in journal (Refereed) Published
Abstract [en]

The waste electrical and electronic equipment (WEEE) recovery can be categorised into two types, i.e. recycling at the material level and remanufacturing at the component level. However, the WEEE recovery is facing enormous challenges of diversified individuals, lack of product knowledge, distributed location, and so forth. On the other hand, the latest ICT provides new methods and opportunities for industrial operation and management. Thus, in this research digital twin and Industry 4.0 enablers are introduced to the WEEE remanufacturing industry. The goal is to provide an integrated and reliable cyber-avatar of the individual WEEE, thus forming personalised service system. The main contribution presented in this paper is the novel digital twin-based system for the WEEE recovery to support the manufacturing/remanufacturing operations throughout the product's life cycle, from design to recovery. Meanwhile, the international standard-compliant data models are also developed to support WEEE recovery services with high data interoperability. The feasibility of the proposed system and methodologies is validated and evaluated during implementations in the cloud and cyber-physical system.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
WEEE, waste electronics, remanufacturing, digital twin, Industry 4, 0
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-259254 (URN)10.1080/00207543.2018.1497819 (DOI)000474250800007 ()2-s2.0-85050302743 (Scopus ID)
Funder
XPRES - Initiative for excellence in production research
Note

QC 20190917

Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-09-17Bibliographically approved
Lian, B., Wang, L. & Wang, X. V. (2019). Elastodynamic modeling and parameter sensitivity analysis of a parallelmanipulator with articulated traveling plate. The International Journal of Advanced Manufacturing Technology
Open this publication in new window or tab >>Elastodynamic modeling and parameter sensitivity analysis of a parallelmanipulator with articulated traveling plate
2019 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed) Published
Abstract [en]

This paper deals with the elastodynamic modeling and parameter sensitivity analysis of a parallel manipulator with articulated traveling plate (PM-ATP) for assembling large components in aviation and aerospace. In the elastodynamic modeling, the PM-ATP is divided into four levels, i.e., element, part, substructure, and the whole mechanism. Herein, three substructures, including translation, bar, and ATP, are categorized according to the composition of the PM-ATP. Based on the kineto-elastodynamic (KED) method, differential motion equations of lower levels are formulated and assembled to build the elastodynamic model of the upper level. Degrees of freedom (DoFs) at connecting nodes of parts and deformation compatibility conditions of substructures are considered in the assembling. The proposed layer-by-layer method makes the modeling process more explicit, especially for the ATP having complex structures and multiple joints. Simulations by finite element software and experiments by dynamic testing system are carried out to verify the natural frequencies of the PM-ATP, which show consistency with the results from the analytical model. In the parameter sensitivity analysis, response surface method (RSM) is applied to formulate the surrogate model between the elastic dynamic performances and parameters. On this basis, differentiation of performance reliability to the parameter mean value and standard variance are adopted as the sensitivity indices, from which the main parameters that greatly affect the elastic dynamic performances can be selected as the design variables. The present works are necessary preparations for future optimal design. They can also provide reference for the analysis and evaluation of other PM-ATPs.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Parallel manipulator, Articulated traveling plate, Elastodynamic modeling, Parameter sensitivity
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-249440 (URN)10.1007/s00170-018-03257-x (DOI)000469002200038 ()2-s2.0-85059859139 (Scopus ID)
Note

QC 20190429

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-10-24Bibliographically approved
Liu, Y., Wang, L., Wang, X. V., Xu, X. & Zhang, L. (2019). Scheduling in cloud manufacturing: state-of-the-art and research challenges. International Journal of Production Research, 57(15-16), 4854-4879
Open this publication in new window or tab >>Scheduling in cloud manufacturing: state-of-the-art and research challenges
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2019 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 57, no 15-16, p. 4854-4879Article, review/survey (Refereed) Published
Abstract [en]

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS LTD, 2019
Keywords
cloud manufacturing, task decomposition, service selection, service composition, scheduling
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-257546 (URN)10.1080/00207543.2018.1449978 (DOI)000479054800012 ()2-s2.0-85044223996 (Scopus ID)
Note

QC 20190925

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-10-11Bibliographically approved
Lian, B., Wang, X. V. & Wang, L. (2019). Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction. Robotics and Computer-Integrated Manufacturing, 59, 267-277
Open this publication in new window or tab >>Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction
2019 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 59, p. 267-277Article in journal (Refereed) Published
Abstract [en]

Having potentially high stiffness and good dynamic response, a parallel pose adjusting mechanism was proposed for being an attachment to a big serial robot of a macro-micro robotic system. This paper addresses its design optimization problem mainly concerning arrangements of design variables and objectives. Parameter changes during construction are added to the design variables in order to prevent the negative effects to the physical prototype. These parameter changes are interpreted as parameter uncertainty and modeled by probabilistic theory. For the objectives, both static and dynamic performances are simultaneously optimized by Pareto-based method. The involved performance indices are instantaneous energy based stiffness index, first natural frequency and execution mass. The optimization procedure is implemented as: (1) carrying out performance modeling and defining performance indices, (2) reformulating statistical objectives and probabilistic constraints considering parameter uncertainty, (3) conducting Pareto-based optimization with the aid of response surface method (RSM) and particle swarm optimization (PSO), (4) selecting optimal solution by searching for cooperative equilibrium point (CEP). By addressing parameter uncertainty and the best compromise among multiple objectives, the presented optimization procedure provides more reliable optimal parameters that would not be affected by minor parameter changes during construction, and less biased optimum between static and dynamic performances comparing with the conventional optimization methods. The proposed optimization method can also be applied to the other similar mechanisms.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Design optimization, Parallel mechanism, Parameter uncertainty, Pareto-based method, Dynamics, Mechanisms, Optimal systems, Stiffness, Uncertainty analysis, Design optimization problem, Parallel mechanisms, Probabilistic constraints, Static and dynamic optimization, Static and dynamic performance, Particle swarm optimization (PSO)
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-252486 (URN)10.1016/j.rcim.2019.04.008 (DOI)000472694400023 ()2-s2.0-85065011128 (Scopus ID)
Note

QC 20190712

Available from: 2019-07-12 Created: 2019-07-12 Last updated: 2019-07-12Bibliographically approved
Wang, L., Gao, R., Vancza, J., Krueger, J., Wang, X. V., Makris, S. & Chryssolouris, G. (2019). Symbiotic human-robot collaborative assembly. CIRP annals, 68(2), 701-726
Open this publication in new window or tab >>Symbiotic human-robot collaborative assembly
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2019 (English)In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 68, no 2, p. 701-726Article in journal (Refereed) Published
Abstract [en]

In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions. 2019 Published by Elsevier Ltd on behalf of CIRP.

Place, publisher, year, edition, pages
ELSEVIER, 2019
Keywords
Assembly, Robot, Human-robot collaboration
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-257832 (URN)10.1016/j.cirp.2019.05.002 (DOI)000481657600005 ()2-s2.0-85066821991 (Scopus ID)
Note

QC 20190905

Available from: 2019-09-05 Created: 2019-09-05 Last updated: 2019-09-05Bibliographically approved
Wang, X. V., Seira, A. & Wang, L. (2018). Classification, personalised safety framework and strategy for human-robot collaboration. In: Proceedings of International Conference on Computers and Industrial Engineering, CIE: . Paper presented at 48th International Conference on Computers and Industrial Engineering, CIE 2018, 2 December 2018 through 5 December 2018. Curran Associates Inc.
Open this publication in new window or tab >>Classification, personalised safety framework and strategy for human-robot collaboration
2018 (English)In: Proceedings of International Conference on Computers and Industrial Engineering, CIE, Curran Associates Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

The modern manufacturing system calls for a safe, efficient and user-friendly working environment to meet the expectation of Industry 4.0 and Smart Manufacturing. The Human-Robot Collaboration is considered as one of the promising approaches and it attracts major research interest in both academia and industry. However, in the past years the reported research results focus more on the advanced robot controlling methods, while the uniqueness of each individual human is not included in the planning or control loop. In this research, multiple types of relationships between the human operator and robot are first classified into four major types. Then the safety framework and strategy is developed towards a personalised solution in a Human-Robot Collaboration cell. The proposed approach is then implemented and evaluated through case studies and quantifiable result comparisons.

Place, publisher, year, edition, pages
Curran Associates Inc., 2018
Keywords
HRC classification, Human-robot collaboration, Safety strategy, Manufacture, Robot programming, Safety engineering, Controlling methods, Research interests, Research results, Result comparison, Smart manufacturing, Working environment, Robots
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247233 (URN)2-s2.0-85061345176 (Scopus ID)
Conference
48th International Conference on Computers and Industrial Engineering, CIE 2018, 2 December 2018 through 5 December 2018
Note

QC 20190402

Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved
Wang, L., Kjellberg, T. J. A., Wang, X. V. & Ji, W. (2018). Editorial: Smart Manufacturing at CIRP CMS 2018. Paper presented at 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm Waterfront Congress CentreStockholm, Sweden, 16 May 2018 through 18 May 2018. Procedia CIRP, 72, 1-2
Open this publication in new window or tab >>Editorial: Smart Manufacturing at CIRP CMS 2018
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1-2Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-238376 (URN)10.1016/j.procir.2018.06.002 (DOI)2-s2.0-85049558462 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm Waterfront Congress CentreStockholm, Sweden, 16 May 2018 through 18 May 2018
Note

QC 20181121

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2018-11-21Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-9694-0483

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