An integrated processing energy modeling and optimization of automated robotic polishing system |
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| Affiliation: | 1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China;2. School of Engineering, University of Liverpool, Liverpool, UK;1. Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26110 Patra, Greece;2. Department of Physics, School of Applied Mathematics and Physical Sciences, National Technical University, Athens 15780, Greece;3. COMAU SpA, Automation Systems, Via Rivalta 49, Grugliasco, Turin 10095, Italy;1. Chair of Automatic Control, Kiel University, Kaiserstr. 2, Kiel 24143, Germany;2. Volkswagen AG, Wolfburg 38436, Germany;1. School of Mechanical Engineering and Automation, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang, Zip code: 110819, People''s Republic of China;2. Liaoning Provincial Key Laboratory of High-end Equipment Intelligent Design and Manufacturing Technology, Northeastern University, No. 3-11, Wen hua Road, He ping District, Shenyang, Zip code: 110819, People''s Republic of China;1. The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics, Department of Mechanical Engineering (DME), Tsinghua University, Beijing 100084, China;2. Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China;3. CRRC Beijing heavy industry Mechanics Co., LTD. Beijing 102249, China |
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| Abstract: | The automated robotic polishing system (ARPS) consisting of several robotic polishing cells (RPCs) is widely adopted in polishing industry to replace manual labor. Recently, energy-saving becomes a hotspot issue in manufacturing industry because of the increase in energy costs and requirement of environmental protection. Traditionally, robot motion planning and task scheduling are carried out separately and sequentially, which constrain the potential for energy-saving. In this paper, a task energy characteristic model is proposed as a polynomial function of the feedrate override to forecast the energy consumption of the polishing process of RPC, in which the designed parameters of the RPC and the polishing process parameters are encapsulated into the polynomial coefficients based on experimental data. Furthermore, an optimization model is proposed for an ARPS with mass tasks to minimize the energy consumption, in which the robot motion planning and the task scheduling are considered integratedly. An adaptive genetic algorithm with elite retention strategy is adopted to solve the optimization model. A case study is introduced to verify the proposed approach, which demonstrates the forecast error of task energy is less than 7%, and the proposed optimization approach can reduce the energy consumption of ARPS by more than 18% compared with the original processing scheme. |
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