利用机器学习发现增材制造中的飞溅本构模型
Discovery of Spatter Constitutive Models in Additive Manufacturing Using Machine Learning
AI总结：
本研究利用机器学习和多项式符号回归构建增材制造飞溅预测框架，基于281种工艺条件的数据集，以熔池尺寸或工艺参数为输入，ExtraTree模型取得最高R2（96.7%和87.5%），为质量控制与缺陷最小化提供决策支持。
摘要：
增材制造（AM）是一项快速发展的技术，因其能够制造复杂几何形状而在广泛领域中获得应用。然而，AM的关键挑战之一是实现一致的打印质量。这种不一致性通常归因于不受控的熔池动力学，部分由可能导致缺陷的飞溅引起。因此，捕捉和控制熔池的演化对于提高工艺稳定性和零件质量至关重要。在本研究中，我们开发了一个支持决策的框架，旨在实现高效的AM工艺操作，能够通过机器学习（ML）和多项式符号回归模型促进质量控制并最小化缺陷。我们实现了经过实验验证的计算工具，特别是针对激光粉末床熔融（LPBF）工艺，作为一种经济高效的方法来收集大型数据集。对于由281种不同工艺条件组成的数据集，提取了熔池尺寸（长度、宽度、深度）、熔池几何形状（面积、体积）以及被标记为飞溅的体积等参数。使用机器学习（ML）和多项式符号回归模型，在训练集和测试集上预测熔池尺寸和几何特征时，以工艺条件（功率和速度）或熔池尺寸作为模型输入，取得了超过95%的高R2。在被标记为飞溅的体积情况下，在对模型输入（工艺条件或熔池尺寸）进行对数变换后，R2值有所提高。在所研究的ML模型中，ExtraTree模型取得了最高的R2值，分别为96.7%和87.5%。
英文摘要：
Additive manufacturing (AM) is a rapidly evolving technology that has attracted applications across a wide range of fields due to its ability to fabricate complex geometries. However, one of the key challenges in AM is achieving consistent print quality. This inconsistency is often attributed to uncontrolled melt pool dynamics, partly caused by spatter which can lead to defects. Therefore, capturing and controlling the evolution of the melt pool is crucial for enhancing process stability and part quality. In this study, we developed a framework to support decision-making towards efficient AM process operations, capable of facilitating quality control and minimizing defects via machine learning (ML) and polynomial symbolic regression models. We implemented experimentally validated computational tools, specifically for laser powder bed fusion (LPBF) processes as a cost-effective approach to collect large datasets. For a dataset consisting of 281 varying process conditions, parameters such as melt pool dimensions (length, width, depth), melt pool geometry (area, volume), and volume indicated as spatter were extracted. Using machine learning (ML) and polynomial symbolic regression models, a high R2 of over 95 % was achieved in predicting the melt pool dimensions and geometry features on both the training and testing datasets, with either process conditions (power and velocity) or melt pool dimensions as the model inputs. In the case of volume indicated as spatter the value of the R2 improved after logarithmic transforming the model inputs, which were either the process conditions or the melt pool dimensions. Among the investigated ML models, the ExtraTree model achieved the highest R2 values of 96.7 % and 87.5 %.
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