Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 275) Show all publications
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
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
Show others...
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
Show others...
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
Show others...
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
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
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
Tao, F., Qi, Q., Wang, L. & Nee, A. Y. (2019). Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. ENGINEERING, 5(4), 653-661
Open this publication in new window or tab >>Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison
2019 (English)In: ENGINEERING, ISSN 2095-8099, Vol. 5, no 4, p. 653-661Article in journal (Refereed) Published
Abstract [en]

State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber-physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber-physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber-physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber-physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives.

Place, publisher, year, edition, pages
ELSEVIER, 2019
Keywords
Cyber-physical systems (CPS), Digital twin (DT), Smart manufacturing, Correlation and comparison
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-260199 (URN)10.1016/j.eng.2019.01.014 (DOI)000483321500015 ()2-s2.0-85068798049 (Scopus ID)
Note

QC 20190930

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-09-30Bibliographically approved
Wang, L. (2019). Editorial: 38th anniversary for Journal of Manufacturing Systems. Journal of manufacturing systems, 51, 132-132
Open this publication in new window or tab >>Editorial: 38th anniversary for Journal of Manufacturing Systems
2019 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 51, p. 132-132Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-255500 (URN)10.1016/j.jmsy.2019.03.001 (DOI)000474312200012 ()2-s2.0-85063580030 (Scopus ID)
Note

QC 20190925

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8679-8049

Search in DiVA

Show all publications