Abstract
In this paper, a complete system based on computer vision and deep learning is proposed for surface inspection of the armatures in a vibration motor with miniature volume. A device for imaging and positioning was designed in order to obtain the images of the surface of the armatures. The images obtained by the device were divided into a training set and a test set. With continuous experimental exploration and improvement, the most efficient deep-network model was designed. The results show that the model leads to high accuracy on both the training set and the test set. In addition, we proposed a training method to make the network designed by us perform better. To guarantee the quality of the motor, a double-branch discrimination mechanism was also proposed. In order to verify the reliability of the system, experimental verification was conducted on the production line, and a satisfactory discrimination performance was reached. The results indicate that the proposed detection system for the armatures based on computer vision and deep learning is stable and reliable for armature production lines.
Highlights
A vibration motor is a source of excitation
In terms of the vibration motors used in digital products, the quality of the motor is an important factor that has an impact on the user experience
The discrimination system can be stably used in the armature production line
Summary
A vibration motor is a source of excitation. Small vibration motors are used in digital products like cell phones to provide a vibration sense. Large vibration motors are used in metallurgy and mining to screen ingredients [1]. In terms of the vibration motors used in digital products, the quality of the motor is an important factor that has an impact on the user experience. In the process of motor production, the armature is assembled into a shell with magnets and bearings [2], so incipient faults in any part of the machinery could produce a chain reaction and lead to its defects [3,4,5]. Due to its miniature volume, it is difficult to detect these defects
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
