Recurrence network analysis of design-quality interactions in additive manufacturing.

Powder bed fusion (PBF) additive manufacturing (AM) provides a great level of flexibility in the design-driven build of metal products. However, the more complex the design, the more difficult it becomes to control the quality of AM builds. The quality challenge persistently hampers the widespread application of AM technology. Advanced imaging (e.g., X-ray computed tomography scans and high-resolution optical images) has been increasingly explored to enhance the visibility of information and improve the AM quality control. Realizing the full potential of imaging data depends on the advent of information processing methodologies for the analysis of design-quality interactions. This paper presents a design of AM experiment to investigate how design parameters (e.g., build orientation, thin-wall width, thin-wall height, and contour space) interact with quality characteristics in thin-wall builds. Note that the build orientation refers to the position of thin-walls in relation to the recoating direction on the plate, and the contour space indicates the width between rectangle hatches. First, we develop a novel generalized recurrence network (GRN) to represent the AM spatial image data. Then, GRN quantifiers, namely degree, betweenness, pagerank, closeness, and eigenvector centralities, are extracted to characterize the quality of layerwise builds. Further, we establish a regression model to predict how the design complexity impacts GRN behaviors in each layer of thin-wall builds. Experimental results show that network features are sensitive to build orientations, width, height, and contour space under the significant level α = 0.05. Thin-walls with the width bigger than 0.1 mm printed under orientation 0° are found to yield better quality compared to 60° and 90°. Also, thin-walls build with orientation 60° are more sensitive to the changes in contour space compare to the other two orientations. As a result, the orientation 60° should be avoided while printing thin-wall structures. The proposed design-quality analysis shows great potential to optimize engineering design and enhance the quality of PBF-AM builds.

Extrusion-based 3D (Bio)Printed Tissue Engineering Scaffolds: Process-Structure-Quality Relationships.

Biological additive manufacturing (Bio-AM) has emerged as a promising approach for the fabrication of biological scaffolds with nano- to microscale resolutions and biomimetic architectures beneficial to tissue engineering applications. However, Bio-AM processes tend to introduce flaws in the construct during fabrication. These flaws can be traced to material nonhomogeneity, suboptimal processing parameters, changes in the (bio)printing environment (such as nozzle clogs), and poor construct design, all with significant contributions to the alteration of a scaffold's mechanical properties. In addition, the biological response of endogenous and exogenous cells interacting with the defective scaffolds could become unpredictable. In this review, we first described extrusion-based Bio-AM. We highlighted the salient architectural and mechanotransduction parameters affecting the response of cells interfaced with the scaffolds. The process phenomena leading to defect formation and some of the tools for defect detection are reviewed. The limitations of the existing developments and the directions that the field should grow in order to overcome said limitations are discussed.

Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, "deep learning" (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data - which we term "deep MVPA," or dMVPA - and introduce a new software toolbox (the "Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education" package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

Paired Trial Classification: A Novel Deep Learning Technique for MVPA.

Many recent developments in machine learning have come from the field of "deep learning," or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. The high dimensionality and large amounts of noise present in EEG data, coupled with the relatively low number of examples (trials) that can be reasonably obtained from a sample of human subjects, lead to difficulty training deep learning models. Even when a model successfully converges in training, significant overfitting can occur despite the presence of regularization techniques. To help alleviate these problems, we present a new method of "paired trial classification" that involves classifying pairs of EEG recordings as coming from the same class or different classes. This allows us to drastically increase the number of training examples, in a manner akin to but distinct from traditional data augmentation approaches, through the combinatorics of pairing trials. Moreover, paired trial classification still allows us to determine the true class of a novel example (trial) via a "dictionary" approach: compare the novel example to a group of known examples from each class, and determine the final class via summing the same/different decision values within each class. Since individual trials are noisy, this approach can be further improved by comparing a novel individual example with a "dictionary" in which each entry is an average of several examples (trials). Even further improvements can be realized in situations where multiple samples from a single unknown class can be averaged, thus permitting averaged signals to be compared with averaged signals.

Process-Structure-Quality Relationships of Three-Dimensional Printed Poly(Caprolactone)-Hydroxyapatite Scaffolds.

Bone defects are common and, in many cases, challenging to treat. Tissue engineering is an interdisciplinary approach with promising potential for treating bone defects. Within tissue engineering, three-dimensional (3D) printing strategies have emerged as potent tools for scaffold fabrication. However, reproducibility and quality control are critical aspects limiting the translation of 3D printed scaffolds to clinical use, which remain to be addressed. To elucidate the factors that yield to the generation of defects in bioprinting and to achieve reproducible biomaterial printing, the objective of this article is to frame a systematic approach for optimizing and validating 3D printing of poly(caprolactone) (PCL)-hydroxyapatite (HAp) composite scaffolds. We delineate the effect of PCL-to-HAp ratio, print velocity, print temperature, and extrusion pressure on the architectural and mechanical properties of the 3D printed scaffold. Furthermore, we present an in situ image-based monitoring approach to quantify key quality-related aspects of constructs, such as the ability to deposit material consistently and print elementary shapes with fewer flaws. Our results show that small defects generated during the printing process have a significant role in lowering the mechanical properties of 3D printed polymeric scaffolds. In addition, the in vitro osteoinductivity of the fabricated scaffolds is demonstrated. Impact statement Identifying quality control measures is essential in the translation of three-dimensional (3D) printed scaffolds into clinical practice. In this article, we highlighted the importance of selected printing parameters on the quality of the 3D printed scaffolds. We also demonstrated that flaws, such as voids, significantly lower the mechanical properties (compressive modulus) of 3D printed polymeric scaffolds.

The role of low-level image features in the affective categorization of rapidly presented scenes.

It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers' rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures.