The Development Of A Quality Prediction System For Aluminum Laser Welding To Measure Plasma Intensity Using Photodiodes

Keywords

Aluminum laser welding; Fuzzy pattern recognition algorithm; Monitoring system; Neural network model; Plasma intensity

Abstract

Lightweight metals have been used to manufacture the body panels of cars to reduce the weight of car bodies. Typically, aluminum sheets are welded together, with a focus on weld quality assurance. A weld quality prediction system for the laser welding of aluminum was developed in this research to maximize welding production. The behavior of the plasma was also analyzed, dependent on various welding conditions. The light intensity of the plasma was altered with heat input and wire feed rate conditions, and the strength of the weld and sensor signals correlated closely for this heat input condition. Using these characteristics, a new algorithm and program were developed to evaluate the weld quality. The design involves a combinatory algorithm using a neural network model for the prediction of tensile strength from measured signals and a fuzzy multi-feature pattern recognition algorithm for the weld quality classification to improve predictability of the system.

Publication Date

10-1-2016

Publication Title

Journal of Mechanical Science and Technology

Volume

30

Issue

10

Number of Pages

4697-4704

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s12206-016-0940-9

Socpus ID

84991573958 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84991573958

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