Abstract
This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.
Highlights
The Shielded Metal Arc Welding (SMAW) process is a simple, low cost and suitable way of joining most metals and alloys commonly used in industry [1]
To compare the results obtained with of different classifiers, we propose a Back Propagation (BP)
Results obtained with the Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers are presented
Summary
The Shielded Metal Arc Welding (SMAW) process is a simple, low cost and suitable way of joining most metals and alloys commonly used in industry [1]. Due to these characteristics, SMAW is the chief joining process used in the developing countries, such as India, China and throughout Latin American [2]. It is a predominantly manual process with low duty cycle. When replacing manual devices with automatic ones, it is necessary to implement a controller based on the process knowledge. Instrumentation and modeling are key issues for the successful implementation of such controllers [5]
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