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  • 1.
    Chen, Zhan
    et al.
    Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China..
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China..
    He, Genghuang
    Xiamen Golden Egret Special Alloy Co Ltd, Xiamen 361006, Peoples R China..
    Liu, Xianli
    Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Rong, Yiming
    Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China..
    Iteration based calculation of position and orientation of grinding wheel for solid cutting tool flute grinding2018In: JOURNAL OF MANUFACTURING PROCESSES, ISSN 1526-6125, Vol. 36, p. 209-215Article in journal (Refereed)
    Abstract [en]

    End mill cutting tools are widely used in machining of curved surface parts in many areas, e.g. aerospace, automobile, and energy. The parameters of helical groove cross section, influencing chip removal capacity, include rake angle, core radius and edge width. Towards a parametrical control algorithm of the cutting tool flute, this paper proposed an iteration based method. Within the context, the cutting tool parameters and grinding wheel are set firstly. Then, three angle parameters, alpha, beta, and theta in the grinding coordinate system, are looped. In each loop, one of the target flute parameters is compared with the pre-set values, and if the parameter is within the range, the calculation goes the next loop until the parameters are in the target range. Since the loop number is so high that the computing time is too long, the patterns between the flute parameters and looped parameters are used to improve the calculation speed. The method is implemented by using C#, and validated by a set of numeric simulation based on Matlab (R).

  • 2.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Li, Y.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A task-oriented cyber-physical system in manufacturing2018In: Proceedings of International Conference on Computers and Industrial Engineering, CIE, Curran Associates Inc. , 2018Conference paper (Refereed)
    Abstract [en]

    Towards automatic operations on a shop floor, this paper proposes a task-oriented cyber-physical system (CPS) concept, called holonic CPS. Within the context, operation processes are separated and modelled independently, and machines are also divided into hardware and basic controllers. The elementary distributed entities from operation processes and machines are represented by holons including “Cyber” and “Physical” parts. A holon is designed in hierarchical structure involving agent, FB, controller and hardware from top to bottom, and a holarchy, a holonic network including relevant holons, is able to represent a task or a function on the shop floor. To show how the proposed holonic CPS works, a robotic application is designed, in which both assembly tasks and machining (grinding and polishing) tasks can be performed. Where a set of relevant holons and holarchies are presented, which provide a “library” as the basis to represent an ordered task. A microkernel architecture is designed to implement the holonic CPS. A major benefit under the holonic CPS allows the partial manufacturing operation to be transferred into computing issues.

  • 3.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.
    Liu, X.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems. Harbin University of Science and Technology, Harbin, China.
    Meng, Y.
    Wu, X.
    A study on geometry modelling of a ball-end mill with chamfered cutting edge2015In: Journal of Manufacturing Processes, ISSN 1526-6125, Vol. 19, p. 205-211, article id 279Article in journal (Refereed)
    Abstract [en]

    This paper presents a geometry modelling approach to cross-section parameters of chamfered cutting edge on a ball-end mill of solid carbide (BEMSC). Both the cutting edge curve and the CR (chamfer in rake face) face models are derived. Based on the CR face model, a new method for CR face grinding path generation is proposed. By determining the relationship between the length and the angle parameters of the CR face equation, its grinding path can be derived. After solving the rake face equation using this method, its grinding path as well as the grinding paths of the LF (land on flank face) face and the second flank face can also be computed. The geometry model has been validated through a series of numerical simulations.

  • 4. Ji, Wei
    et al.
    Liu, X.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Meng, Y.
    Wu, X.
    A study on geometry modelling of ball-end mill with chamfered cutting edge2014In: Transactions of the North American Manufacturing Research Institution of SME, Society of Manufacturing Engineers , 2014, no January, p. 317-324Conference paper (Refereed)
    Abstract [en]

    This paper presents a geometry modelling approach to cross-section parameters of chamfered cutting edge on a ball-end mill of solid carbide (BEMSC). Both the cutting edge curve and the CR (chamfer in rake face) face models are derived. Based on the CR face model, a new method for CR face grinding path generation is proposed. By determining the relationship between the length and the angle parameters of the CR face equation, its grinding path can be derived. After solving the rake face equation using this method, its grinding path as well as the grinding paths of the LF (land on flank face) face and the second flank face can also be computed. The geometry model has been validated through a series of numerical simulations.

  • 5.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Harbin 150080, Peoples R China.
    Liu, Xianli
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Harbin 150080, Peoples R China.
    Sun, Shilong
    Experimental evaluation of polycrystalline diamond (PCD) tool geometries at high feed rate in milling of titanium alloy TC112015In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 77, no 9-12, p. 1549-1555Article in journal (Refereed)
    Abstract [en]

    Titanium alloys are widely used in aerospace industrial components characterised by high material removal rate, of which the machining efficiency is a big issue. Targeting the problem, this paper presents the experimental findings of milling of titanium alloy TC11 using polycrystalline diamond (PCD) cutting tool at high feed rate. First, in order to verify the capability of PCD in finish milling of titanium alloys at high feed rate, the surface roughness R-a is investigated under different PCD tool geometries (radial rake angle, axial rake angle and insert sharp radius), and the results indicate that its range is from 0.821 to 1.562 mu m, which is suitable to titanium components. Also, the main tool failure patterns, cutting edge fracture and flank face wear, are observed and classified. Based on the tool failure patterns, the relationship between tool life and tool geometries is established. In order to explain the reasons of tool failures, the relationships between cutting forces and the tool geometries are made clear. Finally, the processes of flank face wear and rake face wear of PCD insert are proposed to show its wear evaluations.

  • 6.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.
    Shi, J.
    Liu, X.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Liang, S. Y.
    A Novel Approach of Tool Wear Evaluation2017In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 139, no 9, article id 091015Article in journal (Refereed)
    Abstract [en]

    The high-efficiency utilization of cutting tool resource is closely related to the flexible decision of tool life criterion, which plays a key role in manufacturing systems. Targeting a flexible method to evaluate tool life, this paper presents a data-driven approach considering all the machining quality requirements, e.g., surface integrity, machining accuracy, machining stability, chip control, and machining efficiency. Within the context, to connect tool life with machining requirements, all patterns of tool wear including flank face wear and rake face wear are fully concerned. In this approach, tool life is evaluated systematically and comprehensively. There is no generalized system architecture currently, and a four-level architecture is therefore proposed. Workpiece, cutting condition, cutting parameter, and cutting tool are the input parameters, which constrain parts of the independent variables of the evaluation objective including first-level and second-level indexes. As a result, tool wears are the remaining independent variables, and they are calculated consequently. Finally, the performed processes of the method are experimentally validated by a case study of turning superalloys with a polycrystalline cubic boron nitride (PCBN) cutting tool.

  • 7.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems. Harbin University of Science and Technology, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Big Data Analytics Based Optimisation for Enriched Process Planning: A Methodology2017In: Manufacturing Systems 4.0 – Proceedings of the 50th CIRP Conference on Manufacturing Systems, Elsevier, 2017, Vol. 63, p. 161-166Conference paper (Refereed)
    Abstract [en]

    To improve flexibility and accurateness of the optimisation in machining, this paper presents a big data analytics based optimisation method for enriched process planning in the concept of which cutting condition and cutting tool are optimised together and simultaneously. Within the context, the machining factors (workpiece, machining requirement, machine tool, machining process and machining result etc.) are concerned and represented by data attributes. In case that, the new machining resource, new materials and new machining tools etc., can be represented by a group of parameters, so that each machining cases can be treated by data regardless of the relevant experiments, which can enhance practicality and flexibility of potential application in real industry. Also a hybrid method combining neural networks (NN), analytic hierarchy process (AHP), and evolution based algorithm (EBA) or swarm intelligence based algorithm (SIBA) is proposed. NN based model is trained by the big data to improve the accurateness of each single objective, AHP is employed for multi-objective, and EBA or SBA is used to execute the optimising calculation.

  • 8.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Industrial robotic machining: a review2019In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 103, no 1-4, p. 1239-1255Article, review/survey (Refereed)
    Abstract [en]

    For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

  • 9.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Illinois at Chicago, United States.
    Givehchi, Mohammad
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Xianli
    An enriched machining feature based approach to cutting tool selection2018In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 1, p. 1-10Article in journal (Refereed)
    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.

  • 10.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Hongyi
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Interface architecture design for minimum programming in human-robot collaboration2018In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 129-134Conference paper (Refereed)
    Abstract [en]

    Many metal components, especially large-sized ones, need to be ground or deburred after turning or milling to improve the surface qualities, which heavily depends on human interventions. Robot arms, combining movable platforms, are applied to reduce the human work. However, robots and human should work together due to the fact that most of the large-sized parts belong to small-batch products, resulting in a large number of programming for operating a robot and movable platform. Targeting the problem, this paper proposes a new interface architecture towards minimum programming in human-robot collaboration. Within the context, a four-layer architecture is designed: user interface, function block (FB), functional modules and hardware. The user interface is associated with use cases. Then, FB, with embedded algorithms and knowledge and driven by events, is to provide a dynamic link to the relevant application interface (APIs) of the functional modules in terms of the case requirements. The functional modules are related to the hardware and software functions; and the hardware and humans are considered in terms of the conditions on shop floors. This method provides three-level applications based on the skills of users: (1) the operators on shop floors, can operate both robots and movable platforms programming-freely; (2) engineers are able to customise the functions and tasks by dragging/dropping and linking the relevant FBs with minimum programming; (3) the new functions can be added by importing the APIs through programming.

  • 11.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Yin, S.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A Virtual Training Based Programming-Free Automatic Assembly Approach for Future Industry2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 43865-43873, article id 8425978Article in journal (Refereed)
    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.

  • 12.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Yin, Shubin
    Harbin Univ Sci & Technol, Dept Mech Engn, Harbin, Heilongjiang, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A big data analytics based machining optimisation approach2019In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, Vol. 30, no 3, p. 1483-1495Article in journal (Refereed)
    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.

  • 13.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A Context-Aware Safety System for Human-Robot Collaboration2018In: Procedia Manufacturing, Elsevier B.V. , 2018, p. 238-245Conference paper (Refereed)
    Abstract [en]

    Recent advancements in human-robot collaboration have enabled humans and robots to work together in shared manufacturing environment. However, there still exist needs for a context-aware safety system that not only assures human safety but also provides system efficiency. In this paper, the authors present a context-aware safety system that provides safety and efficiency at the same time. The system can plan robotic paths that avoid colliding with humans while still reach target positions in time. Human poses can also be recognised by the system to further increase system efficiency. Different modules, algorithms, and interfaces are introduced in the paper to support the context-aware safety system for human-robot collaboration. A test case is demonstrated to validate the performance of the system. Finally, a summary and future research directions are given.

  • 14.
    Liu, Xianli
    et al.
    School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Chen, Zhan
    School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Iteration-based error compensation for a worn grinding wheel in solid cutting tool flute grinding2019In: Procedia Manufacturing, Elsevier B.V. , 2019, p. 161-167Conference paper (Refereed)
    Abstract [en]

    Solid carbide end mill are widely used in machining of curved surface parts in many areas, e.g. aerospace, automobile, and energy. In the grinding of its manufacturing, grinding wheels are worn out gradually with the grinding number increasing, resulting in the grinding error if there is no proper error compensation. In order to control the flute parameters precision for a worn wheel, this paper proposes an error compensation method by considering a boundary condition determination of a worn wheel based on an iteration-based calculation of position and orientation of grinding wheel. Within the context, the boundary contact condition between the cutting tool and wear wheel is established by identifying the relevant tool profiles associated with the worn and unworn parts of grinding wheel. On top of that, the disk of grinding wheel which generates the rake angle is detailed by considering contact angle. Finally, the method is implemented by using C#, and validated by a set of numeric simulation based on Matlab®.

  • 15.
    Meng, Yue
    et al.
    Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Lee, Chen-Han
    Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430000, Hubei, Peoples R China..
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Xianli
    Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China..
    Plastic deformation-based energy consumption modelling for machining2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 96, no 1-4, p. 631-641Article in journal (Refereed)
    Abstract [en]

    To predict energy consumption in machining, a mathematical modelling method to mimic the cutting energy consumption during machining is proposed in this paper. The established model is based on the law of energy conservation. The mechanical material property coefficients and cutting parameters are included in the model by using material deformation theory and friction calculation which are used to represent the phenomena in machining. Cutting energy of material removal process is refined by analysing the effect of tool edge geometry. In addition, the machining process is divided into two machining elements, linear element and circular arc element, of which energy consumptions are established based on the principal theories above. Calculation method on the instantaneous cutting thickness for circular arc elements is proposed. Finally, a test example is given to validate the proposed modelling approach. With the proposed method, the separate impacts of the factors (e.g. cutting parameters, workpiece, tool) have been analysed and the physical background behind the known experimental dependence of the cutting parameters on cutting energy is revealed.

  • 16.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Kjellberg, Torsten J. A.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Editorial: Smart Manufacturing at CIRP CMS 20182018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1-2Article in journal (Refereed)
  • 17.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China.
    Meng, Yue
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Heilongjiang, Peoples R China.
    Cutting energy consumption modelling for prismatic machining features2019In: 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)
    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.

  • 18.
    Wang, Yuquan
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Hongyi
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Realtime collaborating with an industrial manipulator using a constraint-based programming approach2018In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, p. 105-110Conference paper (Refereed)
    Abstract [en]

    Safety is the first and foremost step on our long journey to a future in which robots are moving out of the cage to collaborate with and assist people in various fields from entertainment to manufacturing. Different from the well-defined structured environment, safe robot control in a workspace with moving objects, e.g. a human, requires us to control the robot motion on the fly. In order to computationally efficiently achieve a feasible solution, we propose a constraint-based programming approach to guarantee the safe human-robot interaction. We use an optimization framework to integrate constraints from two-fold: the robot control constraints that are responsible for a generic robotic task and the online formulated safety constraints that are responsible for safe human-robot interaction. In this way, we preserve the task execution ability of a robot while guarantee the safe human-robot interaction. We validate the proposed approach with a Schunk industrial manipulator. The experimental results confirms the fact that the proposed approach has the potential to enable an industrial manipulator to work with a human coworker side-by-side.

  • 19. Yang, Shucai
    et al.
    Liu, Weiwei
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.
    Liu, Xianli
    Zhu, Jie
    A novel method of experimental evaluation on BTA tool geometries2017In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 91, no 9-12, p. 4253-4261Article in journal (Refereed)
    Abstract [en]

    Towards the problem of closed space of Boring and Trepanning Association (BTA) drill, this paper presents a novel experimental method to evaluate BTA tool geometries, and a turning-based test is conducted to simulate drilling. The three inserts of BTA drill are replaced by the three turning inserts, the rotation of BTA drill is transformed by workpiece rotation in turning, the feed of BTA drill changes into the feed of turning inserts, and the cutting area per BTA insert is simulated by the cutting depth in turning. To implement the approach, three angles, consisting of edge inclination, flank angle and edge declination, are organised by a three-factor and three-level Taguchi experiment for each BTA insert, e.g. outside insert, centre insert and middle insert. Cutting force, chip patterns and chip curl radius are observed and measured to evaluate the insert geometries.

  • 20. Yin, S.
    et al.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    He, G.
    Liu, X.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Experimental evaluation on texture of flank face on tool wear in chamfer milling of stainless steel2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 9-12, p. 2929-2937Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel grinding enabled texture, ‘V’ shaped texture (VST), on flank face. To implement the texture on a cutting performance, a set of chamfering experiments of stainless steel materials used as 3C product shell usually are presented. Cutting forces, surface qualities, and tool wear are measured and compared, of which results show that both smallest surface roughness and longest tool life are achieved by using a 30° VST chamfer tool. By comparing the results, a clear conclusion can be drawn that texture types and angles are not independent factors to cutting performance; therefore, a suitable combination of texture types and texture angles can provide a significant improvement of tool life and surface quality.

  • 21.
    Yin, S.
    et al.
    School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A machine learning based energy efficient trajectory planning approach for industrial robots2019In: Procedia CIRP, Elsevier B.V. , 2019, p. 429-434Conference 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.

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