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Background: The recent advances in the manufacturing industry, mainly fueled by the fourth industrial revolution, and the shortage of skilled welders are boosting research towards intelligent automatic welding. In this context, vision sensing and machine learning have been widely applied to the development of welding status recognition (WSR) systems. However, researchers have mainly focused on developing accurate models putting aside the analysis on computational performance, which is a relevant aspect of the development of real-time WSR systems. Objective: This work aims at devising improved modeling paradigms, considering optimization towards both accuracy and computational performance, for real-time WSR in gas tungsten arc welding. Methods: Shallow learning paradigms are compared with a benchmark deep learning model regarding classification as well as computational performance. For that purpose, metrics that account for the time a model takes to make a prediction and the hard disk space occupied by the model are proposed. The dataset used in this paper comprehends molten pool images acquired for defective and non-defective welding conditions. A dimensionality reduction technique was used to extract features from the data efficiently. For the model creation, a randomized hyperparameter search strategy was adopted. Results: The modeling workflow adopted resulted in an efficient and lightweight model suitable for being deployed as a diagnostic tool. Results have shown that shallow learning jointly with principal component analysis improved both the accuracy and hardware consumption. Specifically, compared to the benchmark deep learning model, the developed classifier presented size 84.82% smaller, and related accuracy improved by 17.32%. Conclusion: The proposed approach has shown to be an efficient way of achieving a compromise between classification and computational performance when developing a predictive model to serve as an online tool for vision-sensing-based WSR. [ABSTRACT FROM AUTHOR]

Background: Due to the acoustoelastic effect, ultrasonic guided waves have been used to estimate mechanical stress in a cheap and nondestructive fashion. Machine learning has been applied to map complex waveforms to stress estimates, though important aspects to construct and deploy real-time monitoring systems, such as accuracy and hardware consumption, to date, have not been concomitantly explored. Objective: The goal of the present paper is to propose a data modeling methodology that optimizes accuracy and computational implementation, towards devising the best practices for real-time ultrasonic-based stress estimation. Methods: We evaluate shallow and deep learning models with dimensionality reduction, which are compared to the most recent work showing overall better results for the approach presented herein both in terms of accuracy and hardware resources consumption. We generated a dataset to evaluate the models on a test rig with a plate measuring (i) ultrasonic guided waves excited by broadband signals and (ii) different stress conditions. The models are created and tested using a Monte-Carlo hold-out procedure with a repeated k-fold randomized hyperparameter search to evaluate their robustness in different stress conditions. Results: We dramatically improve the current state of the art by proposing and evaluating a more efficient model construction procedure. Results show that by using shallow models and principal component analysis, we were able to improve the model performance in terms of predictive power and inference hardware consumption. Specifically, we obtained an accuracy improvement of about 10% and hardware consumption used for inference smaller in three orders of magnitude when compared to the state-of-the-art reported with deep neural network models. Conclusions: Results herein depicted impact the way practitioners may proceed for building efficient embedded predictive models for stress estimation using guided waves, enabling more precise, decentralized, and real-time nondestructive monitoring. [ABSTRACT FROM AUTHOR]

This paper presents the development of an online stress monitoring technique based on Lamb-wave measurements and a convolutional neural network (CNN) for metallic plate-like structures under static and dynamic loadings. Monitoring stress levels in structural components is crucial because stress concentrations and stress redistribution can be precursors of structural damage and failure. Moreover, the stress variations owing to dynamic loadings are directly related to the fatigue life of a structure. First, an aluminum plate specimen is fabricated, and piezoelectric transducers are installed on the specimen. Different levels of static loading are applied to the specimen with a hydraulic loading machine, and the ultrasonic responses are obtained at each loading level. The applied loads (ground truths) are measured by a load cell built into the loading machine. Then, a CNN is designed and trained by defining the Lamb-wave time responses as the input and the measured stress levels as the output. The performance of the trained CNN is evaluated using the blind test data obtained from various static and constant-amplitude cyclic loading conditions. The uniqueness of this study lies in (1) the automated stress estimation without feature extraction and (2) the stress monitoring capability under constant-amplitude cyclic loadings up to a frequency of 5 Hz. [ABSTRACT FROM AUTHOR]