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.

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.

The Assessment of Line-to-Plane Perpendicularity Error Based on Particle Swarm Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 913-918
Author(s):  
Jia Ding Bao ◽  
Rui Zhao ◽  
Bo Xu ◽  
Yong Hou Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Function Optimization ◽  
Multidimensional Space ◽  
Zone Method ◽  
Swarm Optimization ◽  
Space Function ◽  
Minimum Zone ◽  
And Performance

Perpendicularity error has great influence on the quality and performance of the geometrical products, and it is of great importance to guarantee the interchangeability. Particle swarm optimization (PSO) is an extraordinarily useful intelligent optimization algorithm with several advantages of fast convergence rate and easy realization for computer in the multidimensional space function optimization and dynamic target optimization. As a result, it is very accurate and is accordant with the requirement of minimum zone method (MZC) using PSO to associate the base plane of perpendicularity 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
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