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  • 51.
    Ma, Ji
    et al.
    Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
    Feng, Hsi-Yung
    Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Delaunay-Based Triangular Surface Reconstruction from Points via Umbrella Facet Matching2010In: Proceedings of the 6th IEEE Conference on Automation Science and Engineering, IEEE conference proceedings, 2010, p. 580-585Conference paper (Refereed)
    Abstract [en]

    This  paper  presents  an  effective  algorithm  to reconstruct  a  closed  3D  triangular  surface  mesh  from  a  set of unorganized points based on Delaunay triangles. The algorithm essentially  seeks  to  construct  an  optimal  local  2D  manifold surface  (umbrella)  at  each  individual  point  in  parallel.  The underlying principle is that for any point, there always exists a cluster of triangular facets, selected from the Delaunay triangles at the point, to constitute the shape of an opened umbrella. If a triangular  facet  belongs  to  all  three  umbrellas  of  its  three vertices,  the  triangular  facet  is  considered  as a  matched  facet. When all triangular facets of an umbrella are matched facets, the  umbrella  is  regarded  as  a  matched  umbrella  which  fully overlaps with its neighboring umbrellas. A topologically correct triangular surface mesh is then constructed when the matched umbrella  for  every  individual  point  is  found.  The  proposed Umbrella    Facet    Matching    (UFM)    algorithm    has    been implemented and validated using many publicly available point cloud data sets. The algorithm is seen to be of good convergence and  without  the  need  for  further  hole-filling  post-processing. And the reconstructed surface meshes only contain minor shape approximation errors, when compared to the original surfaces of the sampled points.

  • 52.
    Mao, Jin
    et al.
    Department of Mechanical Engineering, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
    Xu, Xun
    Department of Mechanical Engineering, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Newman, Stephen
    Department of Mechanical Engineering, University of Bath, Bath, BA2 7AY, United Kingdom.
    A Statistic Review of Computer-Aided Process Planning Research2010In: Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference: Volume 2, ASME Press, 2010, p. 513-531Conference paper (Refereed)
    Abstract [en]

    Since  the  late  1970’s,  computer-aided  process  planning  (CAPP)  has attracted a large amount of research interest, which has led to a huge volume   of   literature   published   on   this   subject.   The   literature encompasses  both  reviews  and  research  articles.  The  review  articles are mostly technologically oriented. This paper takes a different angle to  look  back  the  CAPP  research,  that  is,  a  statistic  approach.  The paper analyses the journals that have been publishing CAPP research works.    The concept of “Subject Strength” of a journal is introduced and  used  to  gauge  the  level  of  focus  of  a  journal  on  a  particular research  subject/domain,  i.e.  CAPP.  Discussions  about  the  recent CAPP research works are presented in different categories as they fall in. The term “Technology Impact Factor (TIF)” is introduced to assess the  level  of  impact  of  a  particular  technology,  in  terms  of  citation counts.    All  discussions  and  analyses  are  carried  out  based  on  the data   gathered   from   the   Elsevier’s   Scopus   abstract   and   citation database. Finally, a discussion on the future development is presented. The literature suggests that this is the only review article of the similar nature in the first decade of the century.

  • 53.
    Mohammed, Abdullah
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Interfacing Image Processing with Robotic Sketching2012In: Proceedings of the FAIM 2012, 22nd International Conference on Flexible Automation and Intelligent Manufacturing, June 10th-13th, 2012, Helsinki, Finland / [ed] Hasse Nylund, Satu Kantti, Ville Toivonen & Seppo Torvinen, Tampere: Tampere University of Technology, 2012, p. 285-294Conference paper (Refereed)
    Abstract [en]

    In attempt to have a flexible manufacturing environment with adaptive control systems, several researchers tend to integrate vision systems into the industrial systems to achieve that objective; the majority of the research works that have been done in this perspective tend to keep this kind of integration fully automated. Along the same direction, this paper presents an approach for integrating Robotic Sketching and Path Planning with Image Processing, and it introduces at the same time the ability for operators to remotely control and monitor the processing stages. It has been accomplished using network based architecture consisting of a vision system together with a server, a client and an ABB industrial robot. The aims of this paper are: (1) to provide an example that illustrates the benefit of interfacing the image processing techniques with the industrial shop floor system, and (2) to develop a web based system that allows an operator to remotely monitor and control the stages of the application using Java based applet. Further analyses have been done for the processing times of the application; this helped us to address the problem that consumed the largest proportion of the processing time. The work that has been done in this research provides additional supervision ability for the integrated system, and it demonstrates some of the challenges and the obstacles that may face this kind of integration.

  • 54.
    Mohammed, Abdullah
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Remote monitoring and controlling for robotic path following operations2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, p. 27-33Conference paper (Refereed)
    Abstract [en]

    Controlling a robot's movement requires a prior knowledge about the needed path and configurations to accomplish the movement. The lack of this knowledge causes limitations in the robot's adaptability in dynamic environments. The objectives of this paper are: (1) to improve the ability of the robot to follow any arbitrary path defined by an operator, and (2) to provide the ability for an authorized distant operator to access the system for monitoring and controlling both the robot and the stages of the process. The system developed in this research consists of a calibrated network camera, an industrial robot and an application server. The process starts by having a sketch drown by an operator representing the paths that the robot needs to follow, then the operator can remotely take a snapshot of the paths and retrieve the contours that represent the paths; after that the system sends them to the robot controller to perform the task of path following. The results have shown that the system can perform the required task within a relatively short time and with a reasonable level of quality. This research proves that it is possible to build an adaptive robotic system that can follow efficiently any arbitrary path without the need for defining it in advance.

  • 55.
    Mohammed, Abdullah
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Gao, Liang
    Huazhong University of Science and Technology, Hubei, China.
    Minimizing Energy Consumption for Robot Arm Movement2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 25, p. 400-405Article in journal (Refereed)
    Abstract [en]

    Robots are widely used in industry due to their efficiency and high performance. Many of them are operating in the manufacturing stage of the production line where the highest percentage of energy is consumed. Therefore, their energy consumption became a major focus for many robots manufacturers and academic research groups. Nevertheless, the optimisation of that consumption is still a challenging task which requires a deep understanding of the robot’s kinematic and dynamic behaviours. This paper proposes an approach to develop an optimisation module using Matlab® to minimise the energy consumptions of the robot’s movement. With the help of Denavit-Hartenberg notation, the approach starts first by solving the inverse kinematics of the robot to find a set of feasible joint configurations required to perform the task, solving the inverse kinematics is usually a challenging step which requires in-depth analyses of the robot. The module then solves the inverse dynamics of the robot to analyse the forces and torques applied on each joint and link in the robot. Furthermore, a calculation for the energy consumption is performed for each configuration. The final step of the process represents the optimisation of the calculated configurations by choosing the one with the lowest power consumption and sends the results to the robot controller. Three case studies are used to evaluate the performance of the module. The experimental results demonstrate the developed module as a successful tool for energy efficient robot path planning. Further analyses for the results have been done by comparing them with the ones from commercial simulation software. The case studies show that the optimisation of the location for the target path could reduce the energy consumption effectively.

  • 56.
    Mohammed, Abdullah
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Gao, Robert X.
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA.
    Integrated Image Processing and Path Planning for Robotic Sketching2013In: Eighth CIRP Conference on Intelligent Computation in Manufacturing Engineering / [ed] Roberto Teti, Elsevier, 2013, Vol. 12, p. 199-204Conference paper (Refereed)
    Abstract [en]

    Since the beginning of the development of machine vision, researchers have realized its importance in the robotics field, as it provides a useful tool for both the environment detection and decision making during the automation process. At the same time, path planning for robots influences many in the robotics and automation field and it has remained active for both methodology research and system implementation. This research combines machine vision with robot path planning with an aim of programming-free robotic applications. Particularly as a proof of concept, a programming-free robotic sketching prototype is developed as a case study. Within the context, this paper consists of three parts. The first part covers the processing of a facial image taken by a webcam to identify the contours that represent the image; the second part converts these contours to paths for an industrial robot to follow; and the third part controls the robot adaptively for sketching including auto-generation of control codes and self-calibration. The developed prototype is a closed-loop system with networked camera and robot. Intelligent computation is applied to identify the contours of the image with minimum representation of points and with the correct sequence of points for each curve (path); the sequence of the output robot paths represents the near-optimal sequence to preserve the minimum travelling time for the robot. The robot control module can also retrieve the TCP of the robot for off-site monitoring. The ultimate goal of this research is future applications of robot path following, e.g. ad-hoc robotic cutting or welding where the paths can be specified by hand-drawings of an operator on the target workpiece with zero programming for the operator.

  • 57.
    Prasoon, Ruchir
    et al.
    Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Das, Devashish
    Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Tiwari, Manoj Kumar
    Department of Industrial Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    An algorithm portfolio approach to reconfigurable set-up planning2011In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 24, no 8, p. 756-768Article in journal (Refereed)
    Abstract [en]

    This article discusses an algorithm portfolio approach to find optimal set-up plans in a dynamic shop floor environment where flexibility and promptness of the decision process is critical along with best possible utilisation of the available resources. An evolutionary algorithm based reconfigurable set-up planning approach is presented where the final set-up plan is determined in two steps: primitive set-up planning through feature grouping and reconfigurable set-up merging based on real time information from the scheduling system. The tendency of single algorithm approach to converge to sub-optimal solutions was countered by using portfolios of genetic algorithm and its three variants: Genetic Algorithm with Chromosome Differentiation, Sexual Genetic Algorithm and a modified version of Age Genetic Algorithm. Best performing portfolios selected after exhaustive experimentation showed dramatic computational improvements in achieving the optimal solution validating the appropriateness and effectiveness of algorithm portfolio approach.

  • 58.
    Sandberg, Ulf
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Kungliga Tekniska Högskolan.
    Management of factory and maintenance information for multiple production and product life-cycle phases2014In: / [ed] B.K.N.Rao, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance is crucial for future manufacturing systems. An extended local knowledge is essential to increase precision and efficiency, but also for improvements of the maintained object itself. Approaches exist that closes the loop from end-user to vendor, but intra loops are not so well developed.

    This article discusses ways to interconnect and manage data and knowledge flow between work processes in user and vendor life-cycles. It aims to inspire improvements in existing approaches, closer connections between producer and customer, between users, and improved quality of maintenance work via factory-, company-, or group-wide data and knowledge about similar types of equipment.

  • 59.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Diego, Galar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Sweden.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH.
    Asset management evolution: from taxonomies toward ontologies2015In: Maintenance, Condition Monitoring and Diagnostics, Maintenance Performance Measurement and Management / [ed] Sulo Lahdelma and Kari Palokangas, Oulu, Finland: POHTO , 2015Conference paper (Refereed)
    Abstract [en]

    This paper addresses the evolution that can be observed in Asset Management in modelling approach. Most traditional Condition Monitoring systems use hierarchical representations of monitored the integration of data from disparate source toward context awareness and Big Data utilization there is a need to include and model more complicated dependencies than hierarchical. Ontology based modelling is gaining recently on popularity in the domain of Condition Monitoring and Asset Management.

  • 60.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Big data in maintenance decision support systems: aggregation of disparate data types2016In: Euromaintenance 2016 ConferenceProceedings, 2016, p. 503-512Conference paper (Other academic)
    Abstract [en]

    There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.

  • 61.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Context Awareness in Predictive Maintenance2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, p. 197-211Chapter in book (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

  • 62.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed)
    Abstract [en]

    The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

  • 63.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden.
    Galar, Diego
    Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.
    Context preparation for predictive analytics – a case from manufacturing industry2017In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed)
    Abstract [en]

    Purpose

    The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

    Design/methodology/approach

    This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

    Findings

    Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

    Originality/value

    This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

  • 64.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integration of events and offline measurement data from a population of similar entities for condition monitoringIn: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed)
    Abstract [en]

    In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.

  • 65.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Mohammed, Abdullah
    Royal Institute of Technology 100 44 Stockholm, Sweden.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Minimising Energy Consumption for Robot Arm Movement2013In: Proceedings of the International Conference on Advanced Manufacturing Engineering and Technologies / [ed] Andreas Archenti, Antonio Maffei, Stockholm, Sweden: KTH Royal Institute of Technology, 2013, p. 125-134Conference paper (Refereed)
    Abstract [en]

    Optimising the energy consumption of robot movements has been one of the main focuses for most of today’s robotic simulation software. This optimisation is based on minimising a robot’s joints movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot’s kinematic and dynamic models together with application requirements. The primary focus of this research is to develop an optimisation model to reduce the energy consumption in robotic applications. An energy optimisation module reported in this paper was developed using Matlab. By solving the kinematics and dynamics equations of the robot, the module is able to optimise towards the minimum energy consumption of the robot’s movements. Moreover, placement of the targets in robot’s working area that minimise the energy consumption can be suggested. The results show the value of the reported approach as a tool for energy efficient robot path planning.

  • 66.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Sandberg, Ulf
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering Royal Institute of Technology, Sweden.
    Next Generation Condition Based Predictive Maintenance2014In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson, Mats Björkman, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.

  • 67.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Active collision avoidance for human-robot collaborative manufacturing2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, p. 81-86Conference paper (Refereed)
    Abstract [en]

    In the human-robot collaborative manufacturing environment where humans and robots coexist, safety protection of human operators in real time is of paramount importance. This paper presents an approach for real-time active collision avoidance in augmented environment, where virtual 3D models of robots and real camera images of operators are used for monitoring and collision detection. A cost-effective depth camera is chosen for surveillance of any mobile foreign objects, including operators, which are not presented in the virtual 3D models. Two redundant Kinect sensors using structured light are used as the depth cameras for better area coverage and for eliminating possible blind spots in the surveillance area. Collision detection is performed by minumum distance. Processing applied on depth images includes background removal, filtering, labeling and points cloud generation. A prototype system is developed and linked to robot controllers for real-time robot control, with zero robot programming. According to the result of collision detection, it can alert an operator, stop a robot, or even move a robot away from an approaching operator. The results of a case study show that this approach can be applied to real-world applications such as human-robot collaborative assembly to safeguard human operators.

  • 68.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Automatic Robot Calibration via a Global-Local Camera System2012In: Proceedings of FAIM 2012, Tampere University of Technology, 2012Conference paper (Refereed)
    Abstract [en]

    In a human-robot collaborative manufacturing application where working object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic calibration in flexible robotic systems. It consists of two subsystems: a global positioning system based on fixed cameras mounted around robotic workspace, and a local positioning system based on the camera mounted on the robot arm. The aim of the global positioning is to detect work object in working area and roughly estimate the position, whereas the local positioning is to define the object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers have been utilized. For each object, several markers have been used to increase the robustness and accuracy of localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 69.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Automatic work objects calibration via a global-local camera system2014In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 30, no 6, p. 678-683Article in journal (Refereed)
    Abstract [en]

    In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 70.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH.
    Cloud-based Predictive Maintenance2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, p. 224-231Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the presented research is to achieve an improvement in Predictive Condition-based Maintenance Decision Making through the Cloud-based approach with usage of wide information content. For the improvement it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production and factory related data from the first lifecycle phase to the operation and maintenance phase.

  • 71.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Cloud-enhanced predictive maintenance2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

  • 72.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Contact-less and programming-less human-robot collaboration2013In: Forty Sixth CIRP Conference on Manufacturing Systems 2013 / [ed] Pedro F. Cunha, Elsevier, 2013, Vol. 7, p. 545-550Conference paper (Refereed)
    Abstract [en]

    In today's manufacturing environment, safe human-robot collaboration is of paramount importance, to improve efficiency and flexibility. Targeting the safety issue, this paper presents an approach for human-robot collaboration in a shared workplace in close proximity, where real data driven 3D model of a robot and multiple depth images of the workplace are used for monitoring and decision-making to perform a task. The strategy for robot control depends on the current task and the information about the operator's presence and position. A case study of assembly is carried out in a robotic assembly cell with human collaboration. The results show that this approach can be applied in real-world applications such as human-robot collaborative assembly with human operators safeguarded at all time.

  • 73.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Depth camera based collision avoidance via active robot control2014In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, no 4, p. 711-718Article in journal (Refereed)
    Abstract [en]

    A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

  • 74.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Predictive Maintenance: Literature Review and Future Trends2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, p. 232-239Conference paper (Refereed)
    Abstract [en]

    In manufacturing industry machines and systems become more advanced and complicated. Proper maintenance is crucial to ensure productivity, product quality, on-time delivery, and safe working environment. Recently, the importance of the predictive maintenance has been growing rapidly. Well applied predictive maintenance can be in many cases more cost effective than traditional corrective and preventive approaches to maintenance. Targeting this vibrant field, this paper reviews the literature of Predictive Maintenance (PdM). Published literature is systematically categorised and then methodically reviewed and analysed. Methodology for data acquisition, feature extraction, failure detection and prediction are presented. The connection between Maintenance field and Information Fusion has been highlighted. Statistical analysis based on Elsevier’s Scopus abstract and citation database has been performed. Various emerging trends in the field of Predictive Maintenance are identified to help specifying gaps in the literature and direct research efforts.

  • 75.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed)
    Abstract [en]

    In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

  • 76.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Semantic Framework for Predictive Maintenance in a Cloud Environment2017In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, p. 583-588Conference paper (Refereed)
    Abstract [en]

    Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

  • 77.
    Sharon, A.
    et al.
    Boston University.
    Ahmad, M. M.University of Teeside.Hägele, M.Fraunhofer-Institut für Produktionstechnik.Villa, A.Turin Polytechnic.Wang, LihuiUniversity of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    2009Collection (editor) (Other academic)
  • 78.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ayani, Mikel
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Royal Institute of Technology.
    Lindgren-Brewster, Rodney
    Volvo Cars Engine, Skövde, Sweden.
    Localizing Operators in the Smart Factory: A Review of Existing Techniques and systems2016In: Proceedings of 2016 International Symposium on Flexible Automation, IEEE Computer Society, 2016, p. 186-192Conference paper (Refereed)
    Abstract [en]

    The aim of this paper to give a comprehensive overview of existing techniques and state-of-the-art systems for indoor localization that could be adopted in smart factories of the future. We present different techniques for calculating the position of a moving object using signal transmission and signal measurement,and compare their advantages and disadvantages. The paper also includes a discussion of various localization systems available in the market and compares their most important features. It ends with a discussion of important issues to consider in future work in order to fully implement indoor, real-time localization of operators in the smart factory.

  • 79.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Danielsson, Oscar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Royal Institute of Technology, Stockholm, Sweden.
    Dynamic operator instructions based on augmented reality and rule-based expert systems2016In: Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems / [ed] Roberto Teti, Elsevier, 2016, Vol. 41, p. 346-351Conference paper (Refereed)
    Abstract [en]

    Augmented reality is currently a hot research topic within manufacturing and a great potential of the technique is seen. In this study, we aim to increase the knowledge of the adaptation and usability of augmented reality for the training of operators. We propose an approach of using dynamic information content that is automatically adjusted to the individual operator and his/her learning progress for increased efficiency and shorter learning times. The approach make use of the concept of expert systems from the field of artificial intelligence for determine the information content on-line. We develop a framework called "Augmented Reality Expert System" (ARES) that combines AR and expert systems. A proof-of-concept evaluation of the framework is presented in the paper and possible future extensions are discussed.

  • 80.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Danielsson, Oscar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Royal Institute of Technology, Stockholm, Sweden.
    Visual Assembling Guidance Using Augmented Reality2015In: Procedia Manufacturing, ISSN 2351-9789, Vol. 1, p. 98-109Article in journal (Refereed)
    Abstract [en]

    This paper describes a study of using the concept of augmented reality for supporting assembly line workers in carrying out their task optimally. By overlaying virtual information on real world objects – and thereby enhance the human’s perception of reality – augmented reality makes it possible to improve the visual guidance to the workers.  In the study, a prototype system is developed based on the Oculus Rift platform and evaluated using a simulated assembling task. The main aim is to investigate user acceptance and how this can possible be improved. 

  • 81.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Danielsson, Oscar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Royal Institute of Technology, Stockholm, Sweden.
    Lindgren Brewster, Rodney
    Volvo Cars Engine, Skövde, Sweden.
    Support Systems on the Industrial Shop-floors of the Future: Operators' Perspective on Augmented Reality2016In: 6th CIRP Conference on Assembly Technologies and Systems (CATS) / [ed] Rikard Söderberg, Elsevier, 2016, Vol. 44, p. 108-113Conference paper (Refereed)
    Abstract [en]

    With augmented reality, virtual information can be overlaid on the real world in order to enhance a human’s perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator’s perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors.

  • 82.
    Wang, Jinjiang
    et al.
    University of Connecticut, Storrs, CT, USA.
    Gao, Robert X.
    University of Connecticut, Storrs, CT, USA.
    Yan, Ruqiang
    Southeast University, Nanjing, China.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    An EEMD and ICA-based Integrative Approach to Wind Turbine Gearbox Diagnosis2013In: Eighth CIRP Conference on Intelligent Computation in Manufacturing Engineering / [ed] Roberto Teti, Elsevier, 2013, Vol. 12, p. 133-138Conference paper (Refereed)
    Abstract [en]

    Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis.

  • 83.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    A Novel Collaborative Planning Approach for Digital Manufacturing2010In: Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology / [ed] George Q. Huang, K. L. Mak, Paul G. Maropoulos, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2010, p. 939-955Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to develop methodology and algorithms for web-based digital manufacturing, supported by real-time monitoring for dynamic scheduling. This paper presents in particular an integrated approach for developing a web-based system, including distributed process planning, real-time monitoring and remote machining. It is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilizing the latest Java technologies (Java 3D and Java Servlet) for system implementation, this approach allows users to plan and control distant shop floor operations based on runtime information from the shop floor. Details on the principle of the Wise-ShopFloor framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based digital manufacturing.

  • 84.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Alternative Shop-Floor Re-Layout Design due to Dynamic Operation Changes2011In: ASME 2011 International Manufacturing Science and Engineering Conference, Volume 2, ASME Press, 2011, p. 127-133Conference paper (Refereed)
  • 85.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    An ASP Approach to Adaptive Setup Planning and Merging for Available Machines2011Conference paper (Refereed)
  • 86.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Challenges of Adaptive and Collaborative Manufacturing in the 21st Century2010In: Proceedings of the 14th International Conference on Machine Design and Production / [ed] Akkök, M. et al, Middle East Technical University , 2010, p. 39-53Conference paper (Refereed)
    Abstract [en]

    Manufacturing has been one of the key areas that support and influence a nation’s economy since  the  18th  century.  Being  the  primary  driving  force  in  economy  growth,  manufacturing constantly serves as the foundation and contributes to other industries. In the past centuries, manufacturing contributed significantly to modern civilisation and created momentum that is driving today’s  economy. Despite of various revolutionary achievements, we are still facing challenges  when  striving  to  achieve  greater  success  in  manufacturing  in  the  21st  century. This paper highlights the challenges, particularly in adaptive and collaborative manufacturing, and offers a unique approach to solving the problems.

  • 87.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Collaborations towards adaptive manufacturing2012In: Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD) / [ed] Liang Gao, Weiming Shen, Jean-Paul Barthès, Junzhou Luo, Jianming Yong, Wenfeng Li & Weidong Li, IEEE conference proceedings, 2012, p. 14-21Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach for real-time collaborations in adaptive manufacturing, including web-based remote monitoring and control of an industrial robot, and active collision avoidance for human-robot collaborations. It is enabled by using virtual 3D models driven by real sensor data and depth images of human operators. The objectives of this research are to significantly reduce network traffic needed for real-time monitoring over the Internet and to increase the human safety in a human-robot coexisting environment. The results of a case study show that the approach consumes less than 1% of network bandwidth of traditional camera-based methods, and is feasible and practical as a web-based solution.

  • 88.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Combining Facility Layout Redesign and Dynamic Routing for Job-shop Assembly Operations2011In: Proceedings of 2011 IEEE International Symposium on Assembly and Manufacturing, IEEE conference proceedings, 2011, p. artikelnummer 5942302-Conference paper (Refereed)
    Abstract [en]

    This paper presents a hybrid approach for facility layout redesign and dynamic job routing. More specifically, based on the source of uncertainty, the facility layout problem is split into two sub-problems and dealt with by two modules: relayout and find-route. Genetic algorithm is used where changes may cause a layout redesign of the entire shop, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. The method is verified in a case study of a hypothetic robotic assembly shop.

  • 89.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Disassembly Planning by Motion Tracking Analysis2011In: Proceedings of International Conference on Remanufacturing, University of Strathclyde , 2011, p. 182-186Conference paper (Refereed)
    Abstract [en]

    This paper presents an integrated intuitive system for disassembly planning by actively tracking the motion of an experienced operator. It can also be used for operators training by combining a virtual reality (VR) environment with the motion tracking. The developed conceptual prototype for disassembly planning and training enables individuals to interact with a virtual environment in real time. It extends the technology of motion tracking and integrates it with virtual environment technology to create real-time virtual work cell simulations in which disassembly operators may be immersed with hands-on experiences. In addition to the operators training, the experimental results to date are presented to demonstrate the potential contributions of human skills in achieving effective disassembly planning for remanufacturing. It is expected that this approach will lead to environment-friendly and sustainable operations by conserving energy and cost that are first tested in a human-emerged virtual system.

  • 90.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 91.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 92.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
  • 93.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 94.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 95.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 96.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 97.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2009Collection (editor) (Refereed)
  • 98.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2009Collection (editor) (Refereed)
  • 99.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
  • 100.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
123 51 - 100 of 134
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