Investigation of Parameter Spaces for Topology Optimization With Three-Dimensional Orientation Fields for Multi-Axis Additive Manufacturing

The layer-by-layer deposition process used in material extrusion (ME) additive manufacturing results in inter- and intra-layer bonds that reduce the mechanical performance of printed parts. Multi-axis (MA) ME techniques have shown potential for mitigating this issue by enabling tailored deposition directions based on loading conditions in three dimensions (3D). Planning deposition paths leveraging this capability remains a challenge, as an intelligent method for assigning these directions does not exist. Existing literature has introduced topology optimization (TO) methods that assign material orientations to discrete regions of a part by simultaneously optimizing material distribution and orientation. These methods are insufficient for MA–ME, as the process offers additional freedom in varying material orientation that is not accounted for in the orientation parameterizations used in those methods. Additionally, optimizing orientation design spaces is challenging due to their non-convexity, and this issue is amplified with increased flexibility; the chosen orientation parameterization heavily impacts the algorithm’s performance. Therefore, the authors (i) present a TO method to simultaneously optimize material distribution and orientation with considerations for 3D material orientation variation and (ii) establish a suitable parameterization of the orientation design space. Three parameterizations are explored in this work: Euler angles, explicit quaternions, and natural quaternions. The parameterizations are compared using two benchmark minimum compliance problems, a 2.5D Messerschmitt–Bölkow–Blohm beam and a 3D Wheel, and a multi-loaded structure undergoing (i) pure tension and (ii) three-point bending. For the Wheel, the presented algorithm demonstrated a 38% improvement in compliance over an algorithm that only allowed planar orientation variation. Additionally, natural quaternions maintain the well-shaped design space of explicit quaternions without the need for unit length constraints, which lowers computational costs. Finally, the authors present a path toward integrating optimized geometries and material orientation fields resulting from the presented algorithm with MA–ME processes.

High-Energy X-Ray Diffraction Microscopy in Materials Science

High-energy diffraction microscopy (HEDM) is an implementation of three-dimensional X-ray diffraction microscopy. HEDM yields maps of internal crystal orientation fields, strain states, grain shapes and locations as well as intragranular orientation distributions, and grain boundary character. Because it is nondestructive in hard materials, notably metals and ceramics, HEDM has been used to study responses of these materials to external fields including high temperature and mechanical loading. Currently available sources and detectors lead to a spatial resolution of ∼1 μm and an orientation resolution of <0.1○. With the penetration characteristic of high energies ( E ≥ 50 keV), sample cross-section dimensions of ∼1 mm can be studied in materials containing elements across much of the Periodic Table. This review describes hardware and software associated with HEDM as well as examples of applications. These applications include studies of grain growth, recrystallization, texture development, orientation gradients, deformation twinning, annealing twinning, plastic deformation, and additive manufacturing. We also describe relationships to other X-ray-based methods as well as prospects for further development.

A Method for Singular Points Detection Based on Faster-RCNN

Most methods for singular points detection usually depend on the orientation fields of fingerprints, which cannot achieve reliable and accurate detection of poor quality fingerprints. In this study, a new method for fingerprint singular points detection based on Faster-RCNN (Faster Region-based Convolutional Network method) is proposed, which is a two-step process, and an orientation constraint is added in Faster-RCNN to obtain orientation information of singular points. Besides, we designed a convolutional neural network (ConvNet) for singular points detection according to the characteristics of fingerprint images and the existing works. Specifically, the proposed method could extract singular points directly from raw fingerprint images without traditional preprocessing. Experimental results demonstrate the effectiveness of the proposed method. In comparison with other detection algorithms, our method achieves 96.03% detection rate for core points and 98.33% detection rate for delta points on FVC2002 DB1 dataset while 90.75% for core points and 94.87% on NIST SD4 dataset, which outperform other algorithms.

Estimation of fiber orientation and length distribution in cashmere fibrous assemblies

In a general fibrous assembly, single fiber orientation as well as fiber length distributions are important characteristics because they directly influence the properties of textiles. An X-ray micro-tomography experiment with a high resolution of 3 μm was for the first time conducted on a randomly oriented inner Mongolia cashmere fibrous assembly to get a series of two-dimensional projections from different angles and the corresponding cone–beam algorithm proposed by Feldkamp and volume rendering technique were used to realize the three-dimensional (3D) reconstruction. An automated segmentation algorithm described by Rigort and Weber was used to trace and detect single fibers from tomographic 3D data. Local normalized cross-correlation of the tomograms was computed with a cylindrical template that mimics a short microtubule segment to get two new objects, named the correlation and orientation fields. Tracing results of fiber length and orientation distributions were given in this paper statistically.