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Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by the illposedness of inversion at the origin of the spectrum. There are also retrieval ambiguities resulting from the lack of sensitivity to the curl component of the Poynting vector occurring with strong absorption. Here, we establish a physics-informed neural network (PINN) to address these issues, by integrating the forward and inverse physics models into a cascaded deep neural network. We demonstrate that the proposed PINN is efficiently trained using a small set of sample data, enabling the conversion of noise-corrupted 2-shot TIE phase retrievals to high quality phase images under partially coherent LED illumination. The efficacy of the proposed approach is demonstrated by both simulation using a standard image database and experiment using human buccal epitehlial cells. In particular, high image quality (SSIM = 0.919) is achieved experimentally using a reduced size of labeled data (140 image pairs). We discuss the robustness of the proposed approach against insufficient training data, and demonstrate that the parallel architecture of PINN is efficient for transfer learning.

 

Among metal additive manufacturing technologies, additive friction stir deposition stands out for its ability to create freeform and fully-dense structures without melting and solidification. Here, we employ a comparative approach to investigate the process-microstructure linkages in additive friction stir deposition, utilizing two materials with distinct thermomechanical behavior—an Al-Mg-Si alloy and Cu—both of which are challenging to print using beam-based additive processes. The deposited Al-Mg-Si is shown to exhibit a relatively homogeneous microstructure with extensive subgrain formation and a strong shear texture, whereas the deposited Cu is characterized by a wide distribution of grain sizes and a weaker shear texture. We show evidence that the microstructure in Al-Mg-Si primarily evolves by continuous dynamic recrystallization, including geometric dynamic recrystallization and progressive lattice rotation, while the heterogeneous microstructure of Cu results from discontinuous recrystallization during both deposition and cooling. In Al-Mg-Si, the continuous recrystallization progresses with an increase of the applied strain, which correlates with the ratio between the tool rotation rate and travel velocity. Conversely, the microstructure evolution in Cu is found to be less dependent on , instead varying more with changes to . This difference originates from the absence of Cu rotation in the deposition zone, which reduces the influence of tool rotation on strain development. We attribute the distinct process-microstructure linkages and the underlying mechanisms between Al-Mg-Si and Cu to their differences in intrinsic thermomechanical properties and interactions with the tool head. 

Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures. 

Additive friction stir deposition (AFSD) is an emerging solid-state metal additive manufacturing technology renowned for strong interface adhesion and isotropic mechanical properties. This is postulated to result from the material flow phenomena near the interface, but experimental corroboration has remained absent. Here, we seek to understand the interface formed in AFSD via morphological and microstructural investigation, wherein the non-planar interfacial morphology is characterized on the track-scale (centimeter scale) using X-ray computed tomography and the material deformation history is explored by microstructure mapping at the interfacial regions. X-ray computed tomography reveals unique 3D features at the interface with significant macroscopic material mixing. In the out-of-plane direction, the deposited material inserts below the initial substrate surface in the feed-rod zone, while the substrate surface surges upwards in the tool protrusion-affected zone. Complex 3D structures like fins and serrations form on the advancing side, leading to structural interlocking; on the retreating side, the interface manifests as a smooth sloped surface. Microstructure mapping reveals a uniform thermomechanical history for the deposited material, which develops a homogeneous, almost fully recrystallized microstructure. The substrate surface develops partially recrystallized microstructures that are location-dependent; more intra-granular orientation gradients are found in the regions further away from the centerline of the deposition track. From these observations, we discuss the mechanisms for interfacial material flow and interface morphology formation during AFSD. 

Additive friction stir deposition is an emerging solid-state additive manufacturing technology that enables site-specific build-up of high-quality metals with fine, equiaxed microstructures and excellent mechanical properties. By incorporating proper machining, it has the potential to produce large-scale, complex 3D geometries. Still early in its development, a thorough understanding of the thermal process fundamentals, including temperature evolution and heat generation mechanisms, has not been established. Here, we aim to bridge this gap through in situmonitoring of the thermal field and material flow behavior using complementary infrared imaging, thermocouple measurement, and optical imaging. Two materials challenging to print via beam-based additive technologies, Cu and Al-Mg-Si, are investigated. During additive friction stir deposition of both materials, we find similar trends of thermal features (e.g., the trends of peak temperature , exposure time, and cooling rate) with respect to the processing conditions (e.g., the tool rotation rate and in-plane velocity ). However, there is a salient, quantitative difference between Cu and Al-Mg-Si; exhibits a power law relationship with / in Cu but with / in Al-Mg-Si. We correlate this difference to the distinct interfacial contact states that are observed through in situ material flow characterization. In Cu, the interfacial contact between the material and tool head is characterized by a full slipping condition, so interfacial friction is the dominant heat generation mechanism. In Al-Mg-Si, the interfacial contact is characterized by a partial slipping/sticking condition, so both interfacial friction and plastic energy dissipation contribute significantly to the heat generation.