Autofeeding System for Assembling the CBCs on Automobile Engine Based on 3-D Vision Guidance

Abstract

Assembling crankshaft bearing caps (CBCs) on automobile engine is a vital and complicated task that manual work is usually inefficient and become more prone to error. In this article, an autofeeding system with vision and line laser combined with an industrial object location and classification method is proposed for assembling the CBCs. First, an improved fully convolution one-stage network is used to obtain 2-D localization and detect the placement direction and order of a group of CBCs in the CBCs' image. Based on the 2-D location information, the line laser projects the laser on the surface of the CBCs at two different positions, and the images are captured accordingly. Then, an improved Steger algorithm is proposed to extract the centerline of the laser light bar, and the two laser images are merged into one laser image to calculate the height and pose information of the CBCs. The experimental results demonstrate that our method has achieved high real-time performance, accuracy, and robustness. After comparison and application in the factory, it is illustrated that the proposed autofeeding system improves the productivity and saving manpower.