Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Han, Sang-Ok; Zou, Chunlong; Chen, Jianyu; Wang, Shenghuai

doi:10.3389/fbioe.2021.775455

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…Moreover, the algorithms of artificial intelligence were implemented in industry 4.0 to perform surface modeling [51]. In the same way, flat and free-form surface modeling has been developed through the algorithms of artificial intelligence [52,53]. These algorithms perform the parameter optimization by employing the traditional search structure [54,55], where the solution space is not defined through the data related to the surface model.…”

Section: Discussionmentioning

confidence: 99%

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

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Industry 4.0 represents high-level methodologies to make intelligent, autonomous, and self-adaptable manufacturing systems. Additionally, the surface modeling technology has become a great tool in industry 4.0 for representing the surface point cloud. Thus, the micro-scale machining technology requires efficient models to represent micro-scale flat and free-form surfaces. Therefore, it is fundamental to perform surface modeling through artificial intelligence for representing small surfaces. This study addressed multi-objective optimization via genetic algorithms and micro laser line projection to accomplish surface models for representing micro-scale flat and free-form surfaces, where an optical microscope system retrieves micro-scale topography via micro laser line coordinates and the multi-objective optimization constructs the flat and free-form surface models through genetic algorithms and micro-scale topography. The multi-objective optimization determines the surface model parameters through exploration and exploitation, and the solution space is deduced via surface data. The surface model generated through the multi-objective optimization fit accurately to the micro-scale target surface. Thus, the proposed technique enhanced the fitting of micro-scale flat and free-form surface models, which were deduced via gray-level images of an optical microscope. This enhancement was validated by a discussion between the multi-objective optimization via genetic algorithms and the micro-scale surface modeling via optical microscope imaging systems.

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Section: Discussionmentioning

confidence: 99%

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

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“…where k refers to the number of current iteration; ω denotes the inertia weight; and c 2 denote the cognitive learning factor and social learning factor of the particle, respectively, which normally take values between

02

(Xia and Li, 2020 ), signifying the magnitude of the influence exerted by the experience of the particle itself and the population on the position movement of this particle; and r 1 and r 2 represent two numbers between [0, 1] that are generated randomly. It is precisely through the synergistic cooperation and information sharing among the particles that they decide the next movement (Shu et al, 2021 ).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…It is precisely through the synergistic cooperation and information sharing among the particles that they decide the next movement (Shu et al, 2021).…”

Section: Standard Pso Algorithmmentioning

confidence: 99%

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Zhu,

Wu,

Chen

et al. 2023

Front. Neurorobot.

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Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method.

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“…Similar observations can be made when comparing the other provided tables for different degree/machine combinations. The number of points that the laser can capture does not appear to have an effect on the flatness value measured, because even though the number of points widely varied in each scan (Table 4), the recorded data for different replicates does not appear to be changing drastically as seen in figure 8 for FAROLP [8]. Figure 7 shows that the CMM data, while positively skewed, registered the majority of flatness measurements to be just around 0.001in (0.0012in).…”

Section: Robotics and Automation Engineering Journalmentioning

confidence: 99%

A Comparison of CMM and CMA Capabilities in Product Feature Verification Metrology

Raman

2023

RAEJ

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To control variations and guarantee that a part will function as intended in an assembly, measurement verification must be conducted with specialized metrology equipment. The Coordinate Measuring Machine (CMM) and the Articulated Arm CMM (AA-CMM) are analyzed in this comparative study, to identify their respective advantages and disadvantages when measuring flatness. The goal of this pilot study is to detect the measuring limitations of each machine and propose a methodology to identify appropriate use of each device based on its capabilities. For measuring flatness, contact and contactless measurement experiments were designed. As expected, it is observed that the CMM performs better than the CMA in flatness measurement because it has a higher resolution [1]. By looking at the measured flatness obtained during the experiment, it was determined that the CMM had a smaller minimum measurable tolerance in all permutations of the test when compared to its counterpart. Resulting in the proposal of different functional working envelopes for these machines. Establishing working envelopes permits the use of both machines for overlapping working purposes as the versatility of CMAs in Geometry verification in settings where CMMs cannot be deployed, could have a great impact as long as they operate on the corresponding working envelopes defined in this research, which were defined as follows: CMM has a minimum measurable tolerance of 0.0006in, CMA contact, has a minimum measurable tolerance of 0.0024in and CMA contactless, has a minimum measurable tolerance of 0.0045in.

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Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Cited by 9 publications

References 30 publications

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Research on system of ultra-flat carrying robot based on improved PSO algorithm

A Comparison of CMM and CMA Capabilities in Product Feature Verification Metrology

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