Abstract
Assembly is a very important manufacturing process in the age of Industry 4.0. Aimed at the problems of part identification and assembly inspection in industrial production, this paper proposes a method of assembly inspection based on machine vision and a deep neural network. First, the image acquisition platform is built to collect the part and assembly images. We use the Mask R-CNN model to identify and segment the shape from each part image, and to obtain the part category and position coordinates in the image. Then, according to the image segmentation results, the area, perimeter, circularity, and Hu invariant moment of the contour are extracted to form the feature vector. Finally, the SVM classification model is constructed to identify the assembly defects, with a classification accuracy rate of over 86.5%. The accuracy of the method is verified by constructing an experimental platform. The results show that the method effectively completes the identification of missing and misaligned parts in the assembly, and has good robustness.
Highlights
Is a very important process in manufacturing [1]
This paper presents a method based on Mask R-Convolutional neural networks (CNNs) and Support Vector Machine (SVM) that uses the Mask R-CNN segment assembly image to extract feature vectors for classification
A novel approach based on the Mask R-CNN and SVM model was carried out to identify the accuracy of assembly parts with high precision and high efficiency
Summary
Is a very important process in manufacturing [1]. It is hard to prevent machines from having faults related to missing parts and misalignments. These faults in the assembly machines can cause high production downtime and increase running costs [2,3]. Due to rising labor and facility costs, automation and accuracy in assembly have become the clear solution [4]. The use of computer vision systems for assembly inspection has seen a dramatic increase in recent years. Automated assembly machines operate continuously to achieve high production rates [5]. Computer vision technology has been used to provide product inspection, which helps decision making in production systems [6]
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