Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

Version 1 : Received: 12 May 2021 / Approved: 12 May 2021 / Online: 12 May 2021 (13:55:12 CEST)

A peer-reviewed article of this Preprint also exists.

Knaak, C.; von Eßen, J.; Kröger, M.; Schulze, F.; Abels, P.; Gillner, A. A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards. Sensors 2021, 21, 4205. Knaak, C.; von Eßen, J.; Kröger, M.; Schulze, F.; Abels, P.; Gillner, A. A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards. Sensors 2021, 21, 4205.

Journal reference: Sensors 2021, 21, 4205
DOI: 10.3390/s21124205

Abstract

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

Subject Areas

real-time quality prediction; spatio-temporal features; feature importance; recurrent neural network; high-speed infrared imaging; convolutional neural network; lack of fusion (false friends)

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