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首页> 外文期刊>International journal of applied evolutionary computation >A Hybrid Algorithm for Optimization of Machine Vision Based Tool Position Error
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A Hybrid Algorithm for Optimization of Machine Vision Based Tool Position Error

机译：基于机器视觉的刀具位置误差优化的混合算法

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摘要

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.
机译：刀具定位及其错误优化在工程应用中越来越重要。已经开发出许多用于工具磨损和状态评估的机器视觉系统。开发了用于车床刀具位置和验证的机器视觉系统。为了评估已开发系统的性能，使用电荷耦合器件（CCD）相机在车床移动之前和之后捕获了车床刀具的图像。根据以上图像计算出工具所经过的距离。计算得出的（基于图像）与预期刀具运动之间的差异表示基于视觉的刀具位置误差。在本文中，提出了一种新颖的混合算法（AIS-Bat），以优化已开发的视觉系统中的此错误。为了证明所提算法的有效性，将结果与均值技术和bat算法进行了比较，发现所提算法优于其他两种算法。尽管结果看起来很有希望，但在应用误差优化混合算法之前仍然需要更好的图像处理技术。

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