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

Abstract

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,°atness 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 e®ectiveness of the proposed method statistical analysis (t-test) was performed. CFPSO results for large dimension of problem show signi¯cant di®erence in computation time as compared with GA. The CFPSO algorithm provides 27.25%, 7.5% and 6.38% improvements in circularity,°atness 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.

A multi-objective optimization using response surface model coupled with particle swarm algorithm on FSW process parameters

“…ABC has a slow convergence rate, easy to fall in local optimum, and is difficult to find the best out of available feasible solutions. PSO is widely employed to solve the continuous problems because of the simplicity of concept and fewer parametric settings than other population-based optimization algorithms 14 , 15 .…”

Research on Roundness Error Evaluation of Connecting Rod Journal in Crankshaft Journal Synchronous Measurement

“…By comparing the accuracy and convergence time of the two advanced optimization algorithms, it was found that the two algorithms have obtained similar results, while TLBO takes a higher calculation time in comparison to PSO. Pathak [32] applied the constriction factor PSO (CFPSO) algorithm for evaluating different form errors, including minimum zone roundness. The constriction factor was added to the group velocity updating equation to enhance the exploration in initial iterations.…”

A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices

“…The addition of the constriction factor contributes in the CFPSO's convergence attribute being accelerated. For each form issue, a simple minimum zone objective function is mathematically formulated and finally optimized using the proposed CFPSO [31]. PSO with moving particles (MP-PSO) has been reported [32], where some particles have the ability to move on a scale-free network and also variate the collaboration form thru the search space.…”

“…The main aim of this work aim is to control the impact of the previous velocity and to control the particle's behavior of exploration as well as exploitation. Introduction of the inertia-weight improves the performance of PSOs in the terms of convergence speeds and superiority in results [31]. After the inclusion of inertia-weight ( ), the velocity equation is updated as…”