Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs
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Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs Kaiser Asif1 · Lu Zhang2,3
· Sybil Derrible3 · J. Ernesto Indacochea3 · Didem Ozevin3 · Brian Ziebart1
Received: 5 October 2019 / Accepted: 12 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Weld evaluation processes are usually conducted in the post-weld stage. In this way, defects are found after the weld is completed, often resulting in disposal of expensive material or lengthy repair processes. Simultaneously, weld quality inspections tend to be performed manually by a human, even for an automated weld. Therefore, a proper real-time weld quality monitoring method associated with a decision-making strategy is needed to increase the productivity and automaticity in weld. In this study, acoustic emission (AE) as a real-time monitoring method is introduced for gas metal arc weld. The AE system is designed to cover a wide range of frequencies from 5 to 400 kHz. Additionally, the welding parameters (weld current, voltage, gas flow rate, and heat input) are recorded concurrently with AE. Different types of weld defects are artificially created to generate different signals. For the automated decision-making system, machine learning algorithms are used. Several features extracted from the AE and welding parameters feed into a machine learning algorithm. A new AE feature as the rate of AE energy accumulation extracted from time driven AE feature is defined. For decision-making, supervised learning models are trained and evaluated using testing data. General classification methods—such as Logistic Regression—predict each datapoint separately. In this study, Adversarial Sequence Tagging method is applied to predict the presence of four weld states as good, excessive penetration, burn-through, porosity and porosity-excessive penetration. We explore the prediction task as a sequence tagging problem where the label of a data-point depends on its corresponding features as well as neighboring labels. When all the AE features as well as heat input are used in the feature set, the sequence tagging and logistic regression algorithms achieve a prediction accuracy of 91.18% and 82.35%, respectively, as compared to metallographic analysis. Keywords Gas metal arc welding · Acoustic emission · Machine learning
Introduction Automated welding increases the efficiency and productivity in manufacturing. Yet, welding processes and their variables can be significantly complex, leading to poor weld quality and embedded defects (e.g., porosity, excessive penetration, undercutting), which negatively impact the structural capacity of the welded parts. With proper real-time weld monitoring methods, weld defects are expected to be identi-
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Lu Zhang [email protected]
1
Department of Computer Science, University of Illinois at Chicago, Chicago, USA
2
College of Civil Engineering and Architecture, Guilin University of Technology, Guilin, China
3
Department of Civil and Materials Engineering, U
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