结合机器学习的焊接仿真训练元模型,用于快速探索各种焊接顺序场景

M. Asadi, M. Mohseni, M. Kashani, Michael Fernández, Mathew Smith
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引用次数: 2

摘要

变形是焊接结构中的常见问题,因此焊接标准要求在焊接前制定缓解计划。当处理多个焊缝时,最佳的间歇焊接顺序可以通过平衡焊接过程中的瞬态变形来有效地减少变形。然而,考虑到大量可能的组合,即少数焊缝的几千种组合,找到有效的焊接顺序的过程是一项具有挑战性的任务。作为一种可接受的方法,焊接模拟工具允许工程师在不需要多个物理样品的情况下优化焊接顺序。尽管有高效的仿真工具和强大的超级计算机,但仿真工具受到CPU时间的限制,因此在实际设计中并不成熟。为此,我们构建并集成了一种廉价的低保真机器学习(ML)算法和昂贵的高保真仿真。然后对该ML模型进行训练,通过明智选择的模拟训练集来提高保真度,从而构建一个元模型,用于主动探索各种焊接顺序场景。与现有的机器学习算法需要大量的数据集来训练不同,我们的算法选择相对较小的训练集来构建元模型。最后给出了该算法在实际焊接结构工程中的应用实例。
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Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios
Distortion is a common problem in welded structures, and therefore welding standards require a mitigation plan to be in place before welding. When dealing with multiple welds, an optimal intermittent weld sequence can effectively minimize the distortion by counter-balancing the transient distortion during welding. However, the process of finding an effective weld sequence is a challenging task given a large number of possible combinations, i.e. several thousand for a few welds. As an acceptable approach, welding simulation tools allow engineers to optimize a welding sequence without the need for multiple physical samples. Despite efficient simulation tools and powerful supercomputers, yet simulation tools have been limited by CPU time and therefore not mature for practical designs. To this end, we constructed and integrated an inexpensive low-fidelity machine learning (ML) algorithm with the expensive high-fidelity simulation. This ML model was then trained to increase the fidelity by a wisely chosen train set of simulation to construct a meta-model for active exploration of various weld sequence scenarios. As opposed to existing ML algorithms that require an extensive data set to train, our algorithm picks relatively small training set to construct a meta-model. We present an example of our algorithm implemented in a real welded structure project.
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