Assembly Defect Detection of Atomizers Based on Machine Vision

Wang, Jiankun; Hu, Hong; Chen, Long; He, Caiying

doi:10.1145/3351917.3351925

Cited by 6 publications

(1 citation statement)

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“…Reference [13] uses machine vision to detect assembly defects in atomizers. Reference [14] proposed a novel algorithm by minimizing the area of a boundary enclosing partial scan data points for detecting both the pose and assembly defect of tubular joints with the aid of reference ideal models. The non-contact methods are mainly aimed at the wrong detection of assembly status and missing parts.…”

Section: Related Workmentioning

confidence: 99%

Assembly Status Detection Based on Step-by-step Strategy for Robotic Assembly

Chen

Bie

Yao

et al. 2022

J. Phys.: Conf. Ser.

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Assembly status detection is of great significance in the automated assembly of industrial robots. However, in addition to the traditional manual inspection, the existing methods for assembly status detection are mainly through methods such as torque sensors, etc., which are difficult to implement in practical assembly and lack the detection of the entire assembly cycle. To solve this problem, this paper proposes a method for assembly statute detection based on a step-by-step strategy. The first step is to use fast bounding box collision detection to determine whether the robot enters the correct assembly neighborhood. The second step is to use accurate pose information to determine whether the product is assembled correctly. The experiments show that the proposed method can accurately detect the assembly status for the entire assembly cycle with high assembly efficiency.

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Section: Related Workmentioning

confidence: 99%