Abstract Machine learning (ML) approaches are widely used to develop systems or frameworks with the ability to predict the properties of interest by learning and establishing relationships and inferences from data. In the present work, an ML based framework is proposed to predict the evolution of local strain distribution, plastic anisotropy and failure during tensile deformation of AlSi10Mg aluminum alloy produced by selective laser melting (SLM). The framework combines the methods involved in additive manufacturing (AM) and artificial intelligence (AI). This includes printing of test specimens using laser powder bed fusion (LPBF), x-ray computed tomography (CT) scanning to measure internal defects distribution, mechanical testing with digital image correlation (DIC) to get local strain evolution, extraction and coupling of CT and DIC data, and the development, validation and evaluation of an artificial neural network (ANN) model. The experimental data from CT and DIC measurements are used to train, validate and evaluate the proposed ANN model. Microstructural features such as the size, shape, volume fraction and distribution of porosity are used as an input to ANN. The proposed ANN model successfully predicts the evolution of local strains, plastic anisotropy and failure during tensile deformation. The intensity and location of strain hotspots as well as the shape of shear bands and the location of crack initiation are well predicted. The current research demonstrates the applicability of an ML based ANN approach to predict microstructure – property – performance relationships for engineering materials with intricate heterogeneous microstructures such as those produced additively by SLM. The success of the present approach motivates further use of ML techniques, as a mean for accelerated development of new alloys, AM process optimization and its wide scale applicability.

中文翻译：

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一种预测局部应变分布以及增材制造合金塑性各向异性和断裂演化的机器学习框架

摘要 机器学习 (ML) 方法被广泛用于开发具有通过学习和建立数据关系和推理来预测感兴趣的属性的能力的系统或框架。在目前的工作中，提出了一种基于 ML 的框架来预测选择性激光熔化 (SLM) 生产的 AlSi10Mg 铝合金在拉伸变形过程中局部应变分布、塑性各向异性和失效的演变。该框架结合了增材制造 (AM) 和人工智能 (AI) 中涉及的方法。这包括使用激光粉末床融合 (LPBF) 打印测试样本、X 射线计算机断层扫描 (CT) 扫描以测量内部缺陷分布、使用数字图像相关 (DIC) 进行机械测试以获得局部应变演化、CT 的提取和耦合和 DIC 数据，以及人工神经网络 (ANN) 模型的开发、验证和评估。CT 和 DIC 测量的实验数据用于训练、验证和评估所提出的 ANN 模型。微结构特征，如孔隙度的大小、形状、体积分数和分布，被用作人工神经网络的输入。所提出的人工神经网络模型成功地预测了拉伸变形过程中局部应变、塑性各向异性和失效的演变。应变热点的强度和位置以及剪切带的形状和裂纹萌生的位置都得到了很好的预测。当前的研究证明了基于 ML 的 ANN 方法可用于预测具有复杂异质微观结构的工程材料的微观结构 - 性能 - 性能关系，例如由 SLM 增材制造的那些。本方法的成功推动了 ML 技术的进一步使用，作为加速开发新合金、AM 工艺优化及其广泛适用性的手段。