TY - JOUR
T1 - A survey of machine vision-based monitoring methods for abnormalities in molds
AU - Meng, F. W.
AU - Wang, Q.
AU - Li, K. J.
AU - Huang, Z. P.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/11/9
Y1 - 2018/11/9
N2 - Anomaly monitoring in the mold is a mean to ensure its efficient and stable operation. At present, there are many methods for detecting abnormalities in molds, such as tonnage or strain signal analysis, ultrasonic or magnetostatic detection, and machine vision inspection. This paper compares the advantages and disadvantages of the above methods, lists the existing abnormalities of machine vision mold monitoring methods, and the two major problems (illumination changes and camera vibration) in the process of monitoring solutions are summarized. At the end, the paper summarizes and analyzes the development direction and key points of this field.
AB - Anomaly monitoring in the mold is a mean to ensure its efficient and stable operation. At present, there are many methods for detecting abnormalities in molds, such as tonnage or strain signal analysis, ultrasonic or magnetostatic detection, and machine vision inspection. This paper compares the advantages and disadvantages of the above methods, lists the existing abnormalities of machine vision mold monitoring methods, and the two major problems (illumination changes and camera vibration) in the process of monitoring solutions are summarized. At the end, the paper summarizes and analyzes the development direction and key points of this field.
UR - http://www.scopus.com/inward/record.url?scp=85057535498&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/439/3/032086
DO - 10.1088/1757-899X/439/3/032086
M3 - Conference article
AN - SCOPUS:85057535498
SN - 1757-8981
VL - 439
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 3
M1 - 032086
T2 - 2018 International Conference on Advanced Electronic Materials, Computers and Materials Engineering, AEMCME 2018
Y2 - 14 September 2018 through 16 September 2018
ER -