基于宽度学习的铣削表面粗糙度等级检测

Milling Surface Roughness Grade Detection Based on Broad Learning

当前机器视觉表面粗糙度检测所采用的方法大多是根据图像信息人为设计指标或者使用深度学习,但前者计算过程复杂,后者模型训练及分类所需时间较长,并不适用快速评判的在线检测场合。针对此问题,提出一种基于宽度学习的铣削表面粗糙度等级检测方法。通过工业相机获取普通光照环境下铣削工件表面图片,将其输入构建好的宽度学习模型中进行训练,实现对铣削表面粗糙度的等级检测。该方法不仅能够实现特征自提取,而且模型训练速度快,为视觉粗糙度在线测量提供了一种新的策略。

Most of current methods used for machine-vision surface roughness detection are artificially designed indices based on image information or using deep learning.However,the former is a complex computational process,and the latter takes a long time for model training and classification,which is not suitable for online inspection occasions with fast judging.To address this problem,abroad learning based milling surface roughness grade detection method was proposed.The pictures of milling workpiece surface under normal lighting environment were acquired by industrial camera.Then they were input into the constructed broad learning model for training to realize the grade detection of milling surface roughness.The method not only enables feature auto-extraction but also has fast model training,which offers a new strategy for online visual roughness measurement.