Accurately predicting welding performance measures like ultimate strength (UTS), weld bead hardness, and HAZ mechanical hardness is crucial for ensuring the structural integrity and performance of welded components. Multitask learning (MTL) refers to a machine learning approach in which one model is designed
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Accurately predicting welding performance measures like ultimate strength (UTS), weld bead hardness, and HAZ mechanical hardness is crucial for ensuring the structural integrity and performance of welded components. Multitask learning (MTL) refers to a machine learning approach in which one model is designed to handle several interconnected tasks at the same time. Instead of training separate models for each task, MTL shares representations among tasks, allowing them to leverage common patterns while maintaining task-specific distinctions. In this study, we compared two advanced machine learning techniques, namely multitask neural network (MTNN) and stacking ensemble learning, for predicting these parameters based on a shared dataset. A multitask neural network (MTNN) is a specific type of multitask learning (MTL) model that uses a deep neural network architecture to handle multiple related tasks simultaneously. In MTNN, different tasks share some hidden layers while having task-specific output layers. This shared representation allows the model to learn common patterns across tasks while maintaining task-specific outputs. Both methods are evaluated using RMSE and R
2 to determine their predictive accuracy and overall effectiveness. It showed robust prediction strength, as its RMSE outcomes are 0.1288 for UTS, 0.0886 for weld hardness, and 0.1125 for HAZ hardness, whereas R
2 values are 0.6724, 0.9215, and 0.8407, respectively. This underlines that it can generalize well in interrelated tasks. Stacking ensemble learning outperformed MTL in the accuracy of individual tasks: the RMSE for UTS is 0.0263 and R
2 is 0.9863; for weld hardness, it is 0.0467 and 0.9782; and for HAZ hardness, it is 0.1109 and 0.8453. Such results indicate the good ability of ensemble methods to produce highly accurate, task-specific predictions. This comparison reveals the trade-offs between the two approaches. MTL is good in scenarios where the tasks are related and the data are sparse, giving efficient training and good generalization; stacking ensembles work better in the case of accurate, independent predictions. In both cases, they show remarkable potential for improving the predictive power of welding applications, making them suitable precursors to further investigation into hybrid models that bring the best features of both approaches together.
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