Error Optimization of Machine Vision based Tool Movement using a Hybrid CLONALG and PSO Algorithm

A machine vision system with single monochrome CCD camera and backlight was developed for tool positioning and verification. While evaluating the performance of the developed vision system, the experiments showed that the output of machine vision system was not comparable to the output of sensors embedded in motion stages. Inherent factors like Imaging setup, camera calibration, environmental effects etc. are responsible for the error. These errors must be minimized to achieve maximum efficiency of developed vision system. In this paper, a novel hybrid algorithm is proposed to optimize the tool position error. The proposed algorithm comprises of CLONALG (one of the techniques of Artificial Immune System) and Particle Swarm Optimization (PSO) (a global optimization algorithm). Hybrid algorithm is tested on tool movement ranging from 0.020 mm to 7 mm. Performance of proposed algorithm is evaluated and also compared with CLONALG and PSO algorithms individually.