Additive manufacturing (AM), also known as 3D printing, was introduced to design complicated structures/geometries that overcome the manufacturability limitations of traditional manufacturing processes. However, like any other manufacturing technique, AM also has its limitations, such as the need of support structures for overhangs, long build time etc. To overcome these limitations of 3D printing, 4D printing was introduced, which utilizes smart materials and processes to create shapeshifting structures with the external stimuli, such as temperature, humidity, magnetism, etc. The state-of-the-art 4D printing technology focuses on the “form” of the 4D prints through the multi-material variability. However, the quantitative morphing analysis is largely absent in the existing literature on 4D printing. In this research, the inherited material anisotropic behaviors from the AM processes are utilized to drive the morphing behaviors. In addition, the quantitative morphing analysis is performed for designing and controlling the shapeshifting. A material–process–performance 4D printing prediction framework has been developed through a novel dual-way multi-dimensional machine learning model. The morphing evaluation metrics, bending angle and curvature, are obtained and archived at 99% and 93.5% R², respectively. Based on the proposed method, the material and production time consumption can be reduced by around 65–90%, which justifies that the proposed method can re-imagine the digital–physical production cycle.

The selective laser melting (SLM) process provides a more extensive design space and manufacturability. However, it is still hindered by its inaccuracy in dimension and functionality. The distortion in the SLM process affects the dimensional accuracy of the component and may even hinder the SLM process. Still, the distortion mechanism has not been well explained; specifically, the effects from the process parameters and scan strategies on the distortion have not been sufficiently investigated. In this study, a quantitative model that considers displacements, plastic strains, and thermal strains on each layer is developed to analyze the distortion mechanism. The distortion is found to be induced by a residual stress gradient among the layers. Then, a transient numerical method calculates residual stress, plastic strain, and distortion in the SLM process. Different simulations with various layers, scanning speeds, stiffness of support structures, and scan strategies are performed to study the relationship between process parameters and distortion. It can be found that the distortion decreases as the height increases. The distortion increases with the scanning speed, reaching the maximum at 700 mm/s and then dropping. We concluded that increasing the stiffness of the support structures is beneficial to reduce the distortion and changing the scanning direction among layers is useless to reduce the distortion. This study gives a theoretical model to analyze the distortion and provides guidance for reducing distortions in the SLM process.

The effect of a biaxial load on brittle fractures of solids under predominantly elastic deformation and mode I loading conditions is studied in this article. A cross-shaped specimen is used in this work, and the variation of T-stress is achieved by changing the load applied to the arm parallel to the crack. In the tests, under a series of different loads parallel to the crack, a series of load values perpendicular to the crack are obtained, and the stress intensity factor is calculated by FEM (Finite Element Analysis). The test data demonstrate that the apparent fracture toughness of the material varies with the load parallel to the crack and the T-stress, since the T-stress is directly related to the load parallel to the crack. If only the first term of the Williams series solution is used to describe the stress field near the crack tip, the variation of KC is unexplained; therefore, more than one term in the Williams series solutions is used to develop the theory of biaxial effect on solid fracture behavior. The fracture criterion based on this consideration is also used to predict crack instability or crack curving under mode I load conditions. Experimental data are presented and compared with the theory.