Abstract
Monitoring the assembly process is a challenge in the manual assembly of mass customization production, in which the operator needs to change the assembly process according to different products. If an assembly error is not immediately detected during the assembly process of a product, it may lead to errors and loss of time and money in the subsequent assembly process, and will affect product quality. To monitor assembly process, this paper explored two methods: recognizing assembly action and recognizing parts from complicated assembled products. In assembly action recognition, an improved three-dimensional convolutional neural network (3D CNN) model with batch normalization is proposed to detect a missing assembly action. In parts recognition, a fully convolutional network (FCN) is employed to segment, recognize different parts from complicated assembled products to check the assembly sequence for missing or misaligned parts. An assembly actions data set and an assembly segmentation data set are created. The experimental results of assembly action recognition show that the 3D CNN model with batch normalization reduces computational complexity, improves training speed and speeds up the convergence of the model, while maintaining accuracy. Experimental results of FCN show that FCN-2S provides a higher pixel recognition accuracy than other FCNs.
Highlights
During the assembly process of product, if an assembly error is not immediately detected, it may lead to errors and loss of time and money in the subsequent assembly process, and will affect product quality
This paper addresses the problem of assembly action recognition based on the problem of assembly action recognition based on deep learning, and proposes a neural network deep model learning, and proposes neural network model for assembly action recognition and monitoring
The main innovations and contributions of the present study are as follows: contributions of the present study are as follows: (1) We propose a three-dimensional convolutional neural network (3D CNN) model with batch
Summary
During the assembly process of product, if an assembly error is not immediately detected, it may lead to errors and loss of time and money in the subsequent assembly process, and will affect product quality. This paper considers the use of computer vision-based monitoring of assembly process, with the aim of quickly and accurately recognizing the assembly action of the workers, and recognizing different parts from complicated assembled products. In this way, assembly efficiency and quality of the products can be improved. Artificial feature algorithm uses dense trajectories and motion boundary descriptors to represent video features for extraction-based action recognition usuallyaction require a complex data usually preprocessing action recognition. The main motivation of this paper is to monitor the assembly process by recognizing assembly action and recognizing parts from complicated assembled products.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have