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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]