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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low

Christian Knaak¹, Jakob von Eßen¹, Moritz Kröger²

¹Fraunhofer-Institute for Laser Technology ILT, Steinbachstrasse 15, 52074 Aachen, Germany.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

This study introduces an ensemble deep learning model for real-time welding defect detection. The system accurately identifies critical weld flaws using infrared imaging, enhancing manufacturing quality control.

Keywords:

AI edge device convolutional neural network feature importance high-speed infrared imaging lack of fusion (false friends)real-time process monitoring recurrent neural network

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Area of Science:

Manufacturing Engineering
Artificial Intelligence
Materials Science

Background:

Advanced process monitoring is crucial for diagnosing production issues and ensuring final component quality.
Real-time product quality recognition is essential for developing autonomous and self-improving manufacturing systems.

Purpose of the Study:

To investigate a novel ensemble deep learning architecture for intelligent process monitoring and weld defect detection.
To extract and utilize spatio-temporal features from infrared image sequences for identifying critical welding defects.

Main Methods:

Developed a novel ensemble deep learning architecture combining convolutional neural networks (CNN), gated recurrent units (GRU), k-nearest neighbors (kNN), and support vector machines (SVM).
Extracted spatio-temporal features from infrared image sequences, focusing on keyhole and weld pool regions.
Evaluated the architecture against classical machine learning and state-of-the-art deep learning methods using a comprehensive validation scheme and grid search for hyperparameter optimization.

Main Results:

The proposed ensemble deep learning method achieved the highest detection rates and most robust weld defect recognition compared to all investigated methods.
The system effectively located critical welding defects such as lack of fusion, sagging, lack of penetration, and geometric deviations.
The optimized ensemble deep neural network demonstrated low latency (1.1 ms) on embedded computing devices, suitable for real-time applications.

Conclusions:

The novel ensemble deep learning architecture provides an effective solution for real-time, intelligent process monitoring in manufacturing.
The method significantly improves the accuracy and robustness of weld defect recognition, contributing to enhanced quality control.
The system's optimization for embedded devices enables practical implementation in real-time industrial applications.