A general approach for the machining quality evaluation of S-shaped specimen based on POS-SQP algorithm and Monte Carlo method |
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| Affiliation: | 1. Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China;2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China;3. College of Robotics, Beijing Union University, Beijing, 100027, China;1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China;1. Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;2. State Key Lab of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China;1. Operations Research Program, North Carolina State University, 915 Partners Way, Raleigh, NC, 27606, United States;2. Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, 915 Partners Way, Raleigh, NC, 27606, United States;1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China;2. China Acad Machinery Sci & Technol, State Key Lab Adv Forming Technol & Equipment, Beijing, 100044, China;3. Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China;1. Humanoid and Cognitive Robotics Lab, Dept. of Automatics, Biocybernetics and Robotics, Jo?ef Stefan Institute, Ljubljana, Slovenia;2. Faculty of Electrical Engineering, University of Ljubljana, Slovenia |
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| Abstract: | In S-shaped specimens which are frequently used to reflect the machining ability of machine tools, the surface error refers to the distance between the points on the actual machining surface to their relevant points on the design surface. The proper measurement of this error is crucial for evaluating the machining quality of S-shaped specimens. During the process of error measurement, improper registration between the measurement coordinate system and the design coordinate system, as well as neglected uncertainty remain the main obstacles for the quality evaluation of S-shaped specimens. This study proposes a general method for the high-precision machining quality evaluation of S-shaped specimens that overcomes both problems. By applying the non-uniform rational B-spline (NURBS) surface molding technology, the surface of S-shaped specimen was reconstructed. Based on the minimum area principle and the particle swarm optimization-sequential quadratic programming (POS-SQP) algorithm, a surface error model of S-shaped specimen was developed. This model minimizes the maximum distance of the transacted measurement points to the design surface. It can be used to obtain the optimal registration matrix of the measurement coordinate system, with minimal surface error of S-shaped specimen. Additional common algorithms were also adopted to search the optimal registration matrix for comparison. Accounting for the random characteristics of basic parameters and the nonlinearity of surface error model, an uncertainty model of the surface error of S-shaped specimen was established based on the Monte Carlo method. This could obtain the actual tolerance zone of the surface error, according to which, the allowable tolerance zone of the surface error was optimized and a defined evaluation result of machining quality of S-shaped specimen was obtained. Then, a general approach for the evaluation of the machining quality of S-shaped specimen was developed based on POS-SQP algorithm and Monte Carlo method. This approach was implemented in a case study though a series of experiments. The experimental results identified the proposed approach as effective in improving the measurement quality and the evaluation of the machining quality of S-shaped specimens can thus be performed within an allowable tolerance zone. |
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| Keywords: | Surface error modeling Uncertainty analysis Machining quality evaluation S-shaped specimen POS-SQP algorithm Machine tools |
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