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Publications (10 of 237) 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
Yao, B., Zhou, Z., Wang, L., Xu, W., Yan, J. & Liu, Q. (2018). A function block based cyber-physical production system for physical human robot interaction. Paper presented at 46th North American Manufacturing Research Conference (NAMRC), JUN 18-22, 2018, Texas A & M Univ, College Station, TX. Journal of manufacturing systems, 48, 12-23
Open this publication in new window or tab >>A function block based cyber-physical production system for physical human robot interaction
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2018 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 12-23Article in journal (Refereed) Published
Abstract [en]

Human-robot collaboration (HRC) is becoming a trend in manufacturing industry. However, the dramatic changes of requirements from the market put a higher demand for the flexibility of manufacturing systems. Cyber-Physical Production System (CPPS) which offers benefits of autonomy, self-organisation, and interoperability can be adopted to increase the flexibility of manufacturing systems. IEC 61499 (International Electrotechnical Commission) function blocks (FBs) are modularised and reusable software components for distributed industrial control. It is a suitable technology to realise a CPPS. Therefore, CPPS and FBs can be combined to realise the HRC system. This paper proposes a framework and the implementation method of IEC 61499 FB based CPPS for physical human-robot interaction (pHRI) which is type of HRC. An industrial robot based CPPS for pHRI is decomposed into modularised FBs that can be networked to fulfil manufacturing tasks. An energy consumption FB based on a novel empirical energy consumption model is also added to the system for energy consumption monitoring of the Robot. An assembly case is used to demonstrate the feasibility of the proposed system. Results show that the FB based CPPS for pHRI possesses the potential capability for HRC based assembly. The future work is also discussed.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2018
Keywords
Human robot collaboration (HRC), IEC 61499 function block, Cyber-physical production systems (CPPS), Flexibility
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-238156 (URN)10.1016/j.jmsy.2018.04.010 (DOI)000447483800003 ()2-s2.0-85046138377 (Scopus ID)
Conference
46th North American Manufacturing Research Conference (NAMRC), JUN 18-22, 2018, Texas A & M Univ, College Station, TX
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved
Wen, L., Gao, L., Li, X., Wang, L. & Zhu, J. (2018). A Jointed Signal Analysis and Convolutional Neural Network Method for Fault Diagnosis. In: Procedia CIRP: . Paper presented at 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, 16 May 2018 through 18 May 2018 (pp. 1084-1087). Elsevier
Open this publication in new window or tab >>A Jointed Signal Analysis and Convolutional Neural Network Method for Fault Diagnosis
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2018 (English)In: Procedia CIRP, Elsevier, 2018, p. 1084-1087Conference paper, Published paper (Refereed)
Abstract [en]

Fault diagnosis plays a vital role in the modern industry. In this research, a joint vibration signal analysis and deep learning method for fault diagnosis is proposed. The vibration signal analysis is a well-established technique for condition monitoring, and deep learning has shown its potential in fault diagnosis. In the proposed method, the time-frequency technique, named as S transform, is applied to transfer the vibration signals to images, and then an improved convolutional neural network (CNN) is applied to classify these images. The results show the proposed method has achieved the significant improvement.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
convolutional neural network, Fault diagnosis, time-frequency technique, Condition monitoring, Convolution, Deep learning, Failure analysis, Image enhancement, Manufacture, Mathematical transformations, Neural networks, Signal analysis, Vibration analysis, Convolutional Neural Networks (CNN), Learning methods, S transforms, Time-frequency techniques, Vibration signal, Vibration signal analysis, Well-established techniques, Fault detection
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-236393 (URN)10.1016/j.procir.2018.03.117 (DOI)2-s2.0-85049604166 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, 16 May 2018 through 18 May 2018
Note

QC 20181101

Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved
Ji, W., Yin, S. & Wang, L. (2018). A Virtual Training Based Programming-Free Automatic Assembly Approach for Future Industry. IEEE Access, 6, 43865-43873, Article ID 8425978.
Open this publication in new window or tab >>A Virtual Training Based Programming-Free Automatic Assembly Approach for Future Industry
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 43865-43873, article id 8425978Article in journal (Refereed) Published
Abstract [en]

Currently, the automated assembly depends on advance programming, which is suitable to large-batch products assembly; however, it does not fit in the assembly of small-batch products due to the large amount of preparatory work including assembly planning and robot programming. Therefore, those assemblies in small batch largely rely on human interventions, which is a system-level problem. Targeting the problem, this paper presents a novel programming-free automated assembly planning and control approach based on virtual training. Within the context, the 3-D models of products are used, including general assembly features of each component. The features are used in a search-based planner to generate assembly sequence, and to plan assembly path. Then the virtual assembly simulation is carried out based on the generated assembly plan, where the collisions and contacts are captured and passed to the planner to regenerate a new path. The new path is simulated in the virtual world. The simulation process is repeated until an executable strategy is obtained. In the real world, the physical robots perform the actual assembly by following the trained sequences and paths that are calibrated according to the real positions and orientations of the components. A proof-of-concept case study is carried out in robot operating system environment to validate this approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Assembly, assembly features, programming-free, virtual training, E-learning, Personnel training, Planning, Robot programming, Robots, Three dimensional displays, Virtual reality, Automatic assembly, Human intervention, Robot operating systems (ROS), Robot sensing system, Solid model, Virtual assembly simulations, Robotic assembly
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-238042 (URN)10.1109/ACCESS.2018.2863697 (DOI)000443984000001 ()2-s2.0-85051393111 (Scopus ID)
Note

QC 20190115

Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-01-18Bibliographically approved
Wang, L., Fratini, L. & Shih, A. J. (2018). Advancing manufacturing processes research at NAMRC 46. JOURNAL OF MANUFACTURING PROCESSES, 34, 733-733
Open this publication in new window or tab >>Advancing manufacturing processes research at NAMRC 46
2018 (English)In: JOURNAL OF MANUFACTURING PROCESSES, ISSN 1526-6125, Vol. 34, p. 733-733Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-234623 (URN)10.1016/j.jmapro.2018.05.031 (DOI)000442705100001 ()2-s2.0-85048562608 (Scopus ID)
Note

QC 20180913

Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13Bibliographically approved
Wang, L., Fratini, L. & Shih, A. J. (2018). Advancing manufacturing systems research at NAMRC 46. Journal of manufacturing systems, 48, 1-2
Open this publication in new window or tab >>Advancing manufacturing systems research at NAMRC 46
2018 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 1-2Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2018
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-238155 (URN)10.1016/j.jmsy.2018.05.001 (DOI)000447483800001 ()2-s2.0-85046689493 (Scopus ID)
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved
Ji, W., Wang, L., Haghighi, A., Givehchi, M. & Liu, X. (2018). An enriched machining feature based approach to cutting tool selection. International journal of computer integrated manufacturing (Print), 31(1), 1-10
Open this publication in new window or tab >>An enriched machining feature based approach to cutting tool selection
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2018 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 1, p. 1-10Article in journal (Refereed) Published
Abstract [en]

Cutting tools, considered as a basic prerequisite machining resource, are generally selected according to the selected machining methods, which cannot fit in the current manufacturing environment where small- and medium-sized enterprises (SMEs) are the major manufacturers. For the survival of SMEs, it is critical to develop methods for selecting proper cutting tools and reducing machining cost according to product data. Therefore, this study proposes an enriched machining feature (MF)-based approach towards adaptive cutting tool and machining method selection, in which both machinability and machining cost of MF are considered. It includes a two-step workflow: filtering and optimisation. In the filtering process, cutting tools are filtered according to workpiece materials, geometries of MFs and cutting tool inventory, respectively. Here, MF geometries depend on Machining Limit Value decided by sizes and interference relationships of MFs. Also, the client is suggested to choose proper new cutting tools. In the optimisation process, the filtered cutting tools are considered for all the MFs, and machining costs are calculated for each option, in order to select the cheapest one. In particular, if similar cutting tools are required for different MFs, the cutting tool selection for these MFs should be performed altogether.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Machining feature, MF interference, cutting tools, machining method, machining cost
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Identifiers
urn:nbn:se:kth:diva-220999 (URN)10.1080/0951192X.2017.1356472 (DOI)000418757300001 ()2-s2.0-85025436648 (Scopus ID)
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-11Bibliographically approved
Yue, C., Gao, H., Liu, X., Wang, L. & Liang, S. Y. (2018). Analytical prediction of part dynamics and process damping for machining stability analysis. In: Procedia CIRP: . Paper presented at 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, 16 May 2018 through 18 May 2018 (pp. 1463-1468). Elsevier
Open this publication in new window or tab >>Analytical prediction of part dynamics and process damping for machining stability analysis
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2018 (English)In: Procedia CIRP, Elsevier, 2018, p. 1463-1468Conference paper, Published paper (Refereed)
Abstract [en]

The milling of titanium alloy thin-walled parts is confronted with the double challenges of hard to machined materials and hard machined structures. Aiming at the problem that the effect of the damping process is significant and difficult to measure in the process of machining titanium thin-walled parts, the mathematical model of the process damping is established. The tangential and radial ploughing force coefficients that characterize the damping of the process are obtained based on the power spectral density matrix and the principle of energy balance. The structural dynamic modification method is used to solve the nonlinear problem of the workpiece characteristics with the material removal and the change of the relative position of the tool and the workpiece. The time delay differential equation which considers the damping effect of the process and the dynamic characteristic of the workpiece is solved by the full-discretization method, and the three-dimensional stability lobe diagram of the milling thin-walled parts is obtained. A series of experiments have been conducted to verify the accuracy of the stability prediction.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Dynamic characteristics, Process damping, The three-dimensional stability lobe diagram, Thin-walled parts, Titanium alloy, Damping, Differential equations, Dimensional stability, Discrete event simulation, Manufacture, Milling (machining), Spectral density, Structural dynamics, Titanium alloys, Analytical predictions, Power spectral density matrix, Structural dynamic modification, Three-dimensional stability lobes, Time delay differential equation, Thin walled structures
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-236402 (URN)10.1016/j.procir.2018.03.247 (DOI)2-s2.0-85049562615 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, 16 May 2018 through 18 May 2018
Note

QC 20181031

Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-10-31Bibliographically approved
Mohammed, A. & Wang, L. (2018). Brainwaves driven human-robot collaborative assembly. CIRP annals, 67(1), 13-16
Open this publication in new window or tab >>Brainwaves driven human-robot collaborative assembly
2018 (English)In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 67, no 1, p. 13-16Article in journal (Refereed) Published
Abstract [en]

This paper introduces an approach to controlling an industrial robot using human brainwaves as a means of communication. The developed approach starts by establishing a set of training sessions where an operator is enquired to think about a set of defined commands for the robot and record the brain activities accordingly. The results of the training sessions are then used on the shop floor to translate the brain activities to a set of robot control commands. An industrial case study is carried out to assist the operator in coordinating a collaborative assembly task of a car engine manifold.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Assembly, Robot, Brainwaves
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-232638 (URN)10.1016/j.cirp.2018.04.048 (DOI)000438470400004 ()2-s2.0-85046824437 (Scopus ID)
Note

QC 20180801

Available from: 2018-08-01 Created: 2018-08-01 Last updated: 2018-10-16Bibliographically approved
Wang, L. & Ji, W. (2018). Cloud enabled CPS and big data in manufacturing. In: Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing: (pp. 265-292). Pleiades Publishing (9783319895628)
Open this publication in new window or tab >>Cloud enabled CPS and big data in manufacturing
2018 (English)In: Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing, Pleiades Publishing , 2018, no 9783319895628, p. 265-292Chapter in book (Refereed)
Abstract [en]

This paper presents a cloud enhanced cyber-physical system (cloud CPS) in manufacturing by combining CPS and cloud technologies. The cloud CPS is enhanced by using the combined strength of holons, agents and function blocks (FBs). Here, a holarchy of multiple holons is a sub-CPS within cloud CPS, and a logical part and a physical part are involved in each holon, and they mimic the cyber and physical entities of the CPS. They are able to be realised by agents and FBs for the manufacturing processes. In addition, to address the weakness in operation level, big data analytics (BDA) is applied to optimise machining jobs and to predict faults in scheduling. Within the processes, machining relevant factors, including workpiece, machining requirement, machine tools, cutting tool, cutting conditions, machining process and machining results, are represented by data, which is able to solve the many operational issues in cloud CPS.

Place, publisher, year, edition, pages
Pleiades Publishing, 2018
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
Keywords
Big data analytics, Cloud technology, CPS, Machining optimisation, Scheduling
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-238385 (URN)10.1007/978-3-319-89563-5_20 (DOI)2-s2.0-85046830101 (Scopus ID)
Note

QC 20181121

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2018-11-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8679-8049

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