Abstract
Due to the narrowness of space and the complexity of structure, the assembly of cabin parts has become one of the major bottlenecks in the whole manufacturing process. This paper presents a computer vision-based assistant system that integrates assembly planning, assembly training and guidance, assembly status inspection, and assembly quality evaluation to improve the assembly efficiency and quality of cabin products. By using a novel real-time 3D object registration approach, the mixed reality technology is applied to assembly training and guidance of cabin parts. A machine vision-based custom-made inspection system is designed for assembly surplus detection and gap measurement in half-enclosed cabin space. A cabin assembly quality model is proposed on the basis of Markov chain to quantitatively evaluate the cabin assembly quality. A case study is given to demonstrate the practical application of the proposed system.
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