A Hybrid Algorithm for Optimization of Machine Vision Based Tool Position Error

Tool positioning and its error optimization are gaining considerable importance in engineering applications. A number of machine vision systems have been developed for tool wear and conditioning assessment. A machine vision system for lathe tool position and verification was developed. To evaluate the performance of developed system, images of lathe tool were captured before and after the tool movement with a Charge Coupled Device (CCD) camera. The distance traversed by the tool was calculated from the above images. Difference between the calculated (Image based) and the expected tool movement denotes vision based tool position error. In this paper, a novel hybrid (AIS-Bat) algorithm is proposed to optimize this error in the developed vision system. To prove the effectiveness of proposed algorithm, results were compared with mean technique and bat algorithm, it was observed that proposed algorithm outperforms the other two. Although the results seem promising, still there is a need for better image processing techniques before the application of error optimizing hybrid algorithm. A Hybrid Algorithm for Optimization of Machine Vision Based Tool Position Error