The application of additive manufacturing technology has recently shifted from the fabrication of prototypes to functional end-use products. Consequently, the mechanical strength of products has become of significant importance. To quantify the global and local mechanical strength, it is necessary to characterize the micro-structures and their variation within the product. The extent of bonding between adjacent filaments, both within and between layers, as well as porosity are two of the most important parameters that directly contribute to the mechanical strength of parts in extrusion-based additive manufacturing. However, most of the existing models in the literature either significantly underestimate these parameters or fail to quantify or address their variation along the deposition path and build direction. Hence, in this paper, a hybrid physics-based and data-driven approach is proposed to quantify the extent of filament bonding, porosity, and their distribution within a geometry of interest by characterizing the temperature profile of filaments and their deformation. The proposed models for inter-layer and intra-layer bonding have an average accuracy of 95% and 94%, respectively. In addition, it is observed that the porosity variation model performs better for top layers compared to bottom layers with an average of 51% higher accuracy.

增材制造技术的应用近来已从原型制造转变为功能性最终用途产品。因此，产品的机械强度变得非常重要。为了量化整体和局部机械强度，必须表征产品中的微结构及其变化。层内和层之间的相邻细丝之间的粘结程度以及孔隙率是直接影响基于挤出的增材制造中零件机械强度的两个最重要的参数。然而，文献中的大多数现有模型要么大大低估了这些参数，要么无法量化或解决沿沉积路径和构造方向的变化。因此，在本文中，提出了一种基于物理学的混合数据驱动方法，通过表征细丝的温度分布及其变形来量化细丝粘结程度，孔隙率及其在感兴趣的几何形状内的分布。所提出的层间和层内键合模型的平均准确度分别为95％和94％。另外，可以看出，与底层相比，顶层的孔隙度变化模型表现更好，平均精度高出51％。分别。另外，可以看出，与底层相比，顶层的孔隙度变化模型表现更好，平均精度高出51％。分别。另外，可以看出，与底层相比，顶层的孔隙度变化模型表现更好，平均精度高出51％。