Planar straightness error evaluation based on particle swarm optimization

2006 ◽  
Author(s):  
Jian Mao ◽  
Huawen Zheng ◽  
Yanlong Cao ◽  
Jiangxin Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Error Evaluation ◽  
Swarm Optimization ◽  
Straightness Error

Conicity and cylindricity error evaluation using particle swarm optimization

2010 ◽  
Vol 34 (2) ◽  
pp. 338-344 ◽  
Author(s):  
Xiu-Lan Wen ◽  
Jia-Cai Huang ◽  
Dang-Hong Sheng ◽  
Feng-Lin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Error Evaluation ◽  
Swarm Optimization ◽  
Cylindricity Error

Form Error Evaluation of Noncontact Scan Data Using Constriction Factor Particle Swarm Optimization

2017 ◽  
Vol 16 (03) ◽  
pp. 205-226 ◽  
Author(s):  
Vimal Kumar Pathak ◽  
Amit Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Error Evaluation ◽  
Form Error ◽  
Swarm Optimization ◽  
Significant Difference ◽  
Minimum Zone ◽  
Measuring Machine ◽  
Constriction Factor

Form error evaluation of manufactured parts is one of the crucial aspects of precision coordinate metrology. With the advent of technology, the noncontact data acquisition techniques are replacing the conventional machines like coordinate measuring machine (CMM). This paper presents an optimization technique to evaluate minimum zone form errors, namely straightness, circularity, flatness and cylindricity using constriction factor-based particle swarm optimization (CFPSO) algorithm. Addition of constriction factor helps in accelerating the convergence property of CFPSO. Initially, a simple minimum zone objective function is formulated mathematically for each form error and then optimized using the proposed CFPSO. Primarily, the results of the proposed method for form error evaluation are compared with the literature results. Furthermore, the data obtained from noncontact 3D scanner is processed and the results of form error evaluation using CFPSO algorithm are compared with Steinbichler’s INSPECT PLUS software results. It was found that the results obtained using the proposed CFPSO algorithm are fast and better as compared with other evolutionary techniques like genetic algorithm (GA), previous literatures and software results. Furthermore, to ensure effectiveness of the proposed method statistical analysis ([Formula: see text]-test) was performed. CFPSO results for large dimension of problem show significant difference in computation time as compared with GA. The CFPSO algorithm provides 27.25%, 7.5% and 6.38% improvements in circularity, flatness and cylindricity, respectively, in comparison to RE software results, for determination of minimum zone error. Thus, the methodology presented helps in improving the accuracy and for speeding up of the automated inspection process generally performed by CMMs in industries.

scholarly journals Quasiparticle Swarm Optimization for Cross-Section Linear Profile Error Evaluation of Variation Elliptical Piston Skirt

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Xiulan Wen ◽  
Yibing Zhao ◽  
Youxiong Xu ◽  
Danghong Sheng
Keyword(s):  
Particle Swarm Optimization ◽  
Cross Section ◽  
Particle Swarm ◽  
Complex Form ◽  
Error Evaluation ◽  
Profile Error ◽  
Swarm Optimization ◽  
Linear Profile ◽  
Piston Skirt ◽  
Minimum Zone

Variation elliptical piston skirt has better mechanical and thermodynamic properties and it is widely applied in internal combustion engine in recent years. Because of its complex form, its geometrical precision evaluation is a difficult problem. In this paper, quasi-particle swarm optimization (QPSO) is proposed to calculate the minimum zone error and ellipticity of cross-section linear profile, where initial positions and initial velocities of all particles are generated by using quasi-random Halton sequences which sample points have good distribution properties and the particles’ velocities are modified by constriction factor approach. Then, the design formula and mathematical model of the cross-section linear profile of variation elliptical piston skirt are set up and its objective function calculation approach using QPSO to solve the minimum zone cross-section linear profile error is developed which conforms to the ISO/1101 standard. Finally, the experimental results evaluated by QPSO, particle swarm optimization (PSO), improved genetic algorithm (IGA) and the least square method (LSM) confirm the effectiveness of the proposed QPSO and it improves the linear profile error evaluation accuracy and efficiency. This method can be extended to other complex curve form error evaluation such as cam curve profile.

A modified algorithm of Particle Swarm Optimization for form error evaluation

2017 ◽  
Vol 84 (4) ◽  
Author(s):  
Vimal Kumar Pathak ◽  
Amit Kumar Singh ◽  
Ramanpreet Singh ◽  
Himanshu Chaudhary
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Measured Data ◽  
Error Evaluation ◽  
Form Error ◽  
Swarm Optimization ◽  
Form Errors ◽  
Candidate Solution ◽  
Data Points ◽  
Modified Algorithm

AbstractThe set of measured data points acquired from the Coordinate Measuring Machine (CMM) need to be processed and analyzed for evaluating the form errors inside the manufactured components. This paper presents a modified algorithm of particle swarm optimization (MPSO) for assessing the form error from the set of coordinate measured data points. In the classical algorithm of the particle swarm optimization (PSO), the value of the candidate solution is updated from its existing value without actually comparing the value obtained in the consecutive iterations for fitness. This behaviour attributes to a lack of exploitation ability in the defined search space. The proposed algorithm generates new swarm position and fitness solution for the objective function through an improved and modified search equation based on a proposed heuristic step. In this step, the swarm searches around the best solution of the previous iteration for improving the swarm exploitation capability. The particle swarm uses greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for different types of form errors and then optimized using proposed MPSO. Five benchmark functions are used to prove the effectiveness of the modified algorithm, which is verified by comparing its solution and convergence with those obtained from the established algorithms namely PSO and genetic algorithm (GA). Finally, the result of the proposed algorithm for form error evaluation is compared with previous work and other established nature-inspired algorithms. The results demonstrate that the proposed MPSO algorithm is more efficient and accurate than the other conventional heuristic optimization algorithms. Furthermore, it is well suited for form error evaluation using CMM acquired data.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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