Ultrasonic Rough Crack Characterization Using Time-of-Flight Diffraction With Self-Attention Neural Network.

Time-of-flight diffraction (ToFD) is a widely used ultrasonic nondestructive evaluation (NDE) method for locating and characterizing rough defects, with high accuracy in sizing smooth cracks. However, naturally grown defects often have irregular surfaces, complicating the received tip diffraction waves and affecting the accuracy of defect characterization. This article proposes a self-attention (SA) deep learning method to interpret the ToFD A-scan signals for sizing rough defects. A high-fidelity finite-element (FE) simulation software Pogo is used to generate the synthetic datasets for training and testing the deep learning model. Besides, the transfer learning (TL) method is used to fine-tune the deep learning model trained by the Gaussian rough defects to boost the performance of characterizing realistic thermal fatigue rough defects. An ultrasonic experiment using 2-D rough crack samples made by additive manufacturing is conducted to validate the performance of the developed deep learning model. To demonstrate the accuracy of the proposed method, the crack characterization results are compared with those obtained using the conventional Hilbert peak-to-peak sizing method. The results indicate that the deep learning method achieves significantly reduced uncertainty and error in rough defect characterization, in comparison with traditional sizing approaches used in ToFD measurements.

Imperfection-Enabled Strengthening of Ultra-Lightweight Lattice Materials.

Lattice materials are an emerging family of advanced engineering materials with unique advantages for lightweight applications. However, the mechanical behaviors of lattice materials at ultra-low relative densities are still not well understood, and this severely limits their lightweighting potential. Here, a high-precision micro-laser powder bed fusion technique is dveloped that enables the fabrication of metallic lattices with a relative density range much wider than existing studies. This technique allows to confirm that cubic lattices in compression undergo a yielding-to-buckling failure mode transition at low relative densities, and this transition fundamentally changes the usual strength ranking from plate > shell > truss at high relative densities to shell > plate > truss or shell > truss > plate at low relative densities. More importantly, it is shown that increasing bending energy ratio in the lattice through imperfections such as slightly-corrugated geometries can significantly enhance the stability and strength of lattice materials at ultra-low relative densities. This counterintuitive result suggests a new way for designing ultra-lightweight lattice materials at ultra-low relative densities.

Superelastic NiTi Functional Components by High-Precision Laser Powder Bed Fusion Process: The Critical Roles of Energy Density and Minimal Feature Size.

Additive manufacturing (AM) was recently developed for building intricate devices in many fields. Especially for laser powder bed fusion (LPBF), its high-precision manufacturing capability and adjustable process parameters are involved in tailoring the performance of functional components. NiTi is well-known as smart material utilized widely in biomedical fields thanks to its unique superelastic and shape-memory performance. However, the properties of NiTi are extremely sensitive to material microstructure, which is mainly determined by process parameters in LPBF. In this work, we choose a unique NiTi intricate component: a robotic cannula tip, in which material superelasticity is a crucial requirement as the optimal object. First, the process window was confirmed by printing thin walls and bulk structures. Then, for optimizing parameters precisely, a Gyroid-type sheet triply periodic minimal-surface (G-TPMS) structure was proposed as the standard test sample. Finally, we verified that when the wall thickness of the G-TPMS structure is smaller than 130 µm, the optimal energy density changes from 167 J/m3 to 140 J/m3 owing to the lower cooling rate of thinner walls. To sum up, this work puts forward a novel process optimization methodology and provides the processing guidelines for intricate NiTi components by LPBF.

Achieving Triply Periodic Minimal Surface Thin-Walled Structures by Micro Laser Powder Bed Fusion Process.

Recently, triply periodic minimal surface (TPMS) lattice structures have been increasingly employed in many applications, such as lightweighting and heat transfer, and they are enabled by the maturation of additive manufacturing technology, i.e., laser powder bed fusion (LPBF). When the shell-based TPMS structure's thickness decreases, higher porosity and a larger surface-to-volume ratio can be achieved, which results in an improvement in the properties of the lattice structures. Micro LPBF, which combines finer laser beam, smaller powder, and thinner powder layer, is employed in this work to fabricate the thin-walled structures (TWS) of TPMS lattice by stainless steel 316 L (SS316L). Utilizing this system, the optimal parameters for printing TPMS-TWS are explored in terms of densification, smoothness, limitation of thickness, and dimensional accuracy. Cube samples with 99.7% relative density and a roughness value of 2.1 µm are printed by using the energy density of 100 J/mm3. Moreover, a thin (100 µm thickness) wall structure can be fabricated through optimizing parameters. Finally, the TWS samples with various TPMS structures are manufactured to compare their heat dissipation capability. As a result, TWS sample of TPMS lattice exhibits a larger temperature gradient in the vertical direction compared to the benchmark sample. The steady-state temperature of the sample base presents a 7 K decrease via introducing TWS.