made possible by modern laser welding equipment. Can the inspection be simplified enough to be automated? Yanxi Zhang and Xiangdong Gao of Guangdong Univer-
sity of Technology (Guangzhou, China) and Seiji Katayama of Osaka University’s Joining and Welding Research Institute (Osaka, Japan) joined together to throw some light on the subject. They made a high-speed visual sensing system that used an auxiliary di- ode laser light to illuminate molten pools and cast their shadow. A high-speed (5000 frames/sec) camera captures visual morphology information, recording the area of a casted shadow, maximal distance between points in the shadow and the keyhole position, the maximal width and the tilt of the shadow. In “Weld Appearance Prediction with BP Neural Network Improved by Genetic Algorithm during Disk Laser Welding,” published in SME’s Jour- nal of Manufacturing Processes, the scientists describe how they applied principal components analysis (PCA) ito analyze the characteristics of the molten pools’ shadow in order to re- duce their redundancy. Then BP neural network improved by genetic algorithm (GABP) was used to model the relation between welding appearance and the characteristics of the molten-pool- shadows. The effectiveness of this model was verified through two differ- ent welding speed experiments.
Tech Front welcomes your manufacturing research-related news releases: Please email them to Tech Front editor
Michael Anderson at
manderson@sme.org.
SME’s authoritative peer-reviewed research journals, Journal of Manufacturing Processes, Journal of Manufacturing Systems, and Manufacturing Letters, and technical papers may be reached via
www.sme.org/publications. The journals welcome submissions: Information available at
www.sciencedirect.com/sme.
ManEng_Image-HPI4C.indd 1
This work provides an effective way to assess laser weld
quality in real-time. Read the entire paper at http://tinyurl. com/JMS-weldshadow. Tech Front is edited by Senior Editor Michael C. Anderson
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January 2016 |
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