Improvements in digital image correlation and application in material mechanical test

Digital image correlation (DIC) is a non-contact full-field optical measurement method. With the advantages of high accuracy, low cost, and simple implementation, it has been widely applied in the area of experimental mechanics. In this study, DIC algorithm has been improved in the aspects of the pixel-level searching method and reference frame update strategy. The feature matching based method is proposed to provide an initial guess for all points of interest with semi-subpixel level accuracy in cases with small or large translation, deformation, or rotation. The bisection searching strategy is presented to automatically adjust the frame step for varying practical circumstances. The improved DIC algorithm is implemented and applied to the miniature tensile test. A convenient experimental method to determine the true stress-strain curve is proposed. The instantaneous cross-section area is estimated by only one camera in aid of DIC method. The derived true stress-strain curves and mechanical parameters of metal material Al6061 and CP-Ti from miniature specimens match well with the results of standard specimens, and no dimension dependence has been observed in the results --Abstract, page iv

A Localized Coverage Preserving Protocol for Wireless Sensor Networks

In a randomly deployed and large scale wireless sensor network, coverage-redundant nodes consume much unnecessary energy. As a result, turning off these redundant nodes can prolong the network lifetime, while maintaining the degree of sensing coverage with a limited number of on-duty nodes. None of the off-duty eligibility rules in the literature, however, are sufficient and necessary conditions for eligible nodes. Hence redundancy or blind points might be incurred. In this paper we propose a complete Eligibility Rule based on Perimeter Coverage (ERPC) for a node to determine its eligibility for sleeping. ERPC has a computational complexity of O(N2log(N)), lower than the eligibility rule in the Coverage Control Protocol (CCP), O(N3), where N is the number of neighboring nodes. We then present a Coverage Preserving Protocol (CPP) to schedule the work state of eligible nodes. The main advantage of CPP over the Ottawa protocol lies in its ability to configure the network to any specific coverage degree, while the Ottawa protocol does not support different coverage configuration. Moreover, as a localized protocol, CPP has better adaptability to dynamic topologies than centralized protocols. Simulation results indicate that CPP can preserve network coverage with fewer active nodes than the Ottawa protocol. In addition, CPP is capable of identifying all the eligible nodes exactly while the CCP protocol might result in blind points due to error decisions. Quantitative analysis and experiments demonstrate that CPP can extend the network lifetime significantly while maintaining a given coverage degree

Metal Additive Manufacturing Parts Inspection using Convolutional Neural Network

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in theAMindustry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry

On the Feasibility of Tailoring Copper-Nickel Functionally Graded Materials Fabricated through Laser Metal Deposition

In this study, pulse‐width modulation of laser power was identified as a feasible means for varying the chemical gradient in copper—nickel‐graded materials. Graded material deposits of 70 wt. %. copper‐30 wt. %. nickel on 100 wt. %. nickel and vice versa were deposited and characterized. The 70/30 copper—nickel weight ratio in the feedstock powder was achieved through blending elemental copper and 96 wt. %. Ni—Delero‐22 alloy. At the dissimilar material interface over the course of four layers, the duty cycle of power was ramped down from a high value to optimized deposition conditions. This change was theorized to influence the remelting and deposition height, and by extension, vary the chemistry gradient. X‐ray Energy Dispersive Spectroscopy (EDS) analysis showed significant differences in the span and nature of chemistry gradient with varying duty cycles. These observations were also supported by the variation in microhardness values across the interface. The influence of different chemistry gradients on the tensile performance was observed through mini‐tensile testing, coupled with Digital Image Correlation (DIC). The strain fields from the DIC analysis showed variations in strain for different chemistry gradients. The strength measurements from these specimens were also different for different chemistry gradients. The site of failure was observed to always occur within the copper-rich region

CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification

In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have emerged as effective alternatives to traditional preprocessing data augmentation techniques. Nonetheless, these methods fail to fully consider the unique features of pathology images, such as local specificity, global distribution, and inner/outer-sample instance relationships. To better comprehend these characteristics and create valuable pseudo samples, we propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. By dividing images into patches based on the granularity of pathology instances and shuffling them within the same batch, the absolute relationships between instances can be effectively preserved when generating new samples. Moreover, we develop a curriculum learning-inspired, loss-driven strategy to handle perturbations and distribution-related noise during training, enabling the model to adaptively fit the augmented data. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This innovative instance relationship-centered method has the potential to inform general data augmentation approaches for pathology image classification. The associated codes are available at https://github.com/sagizty/CellMix

A Comparative Study on Representativeness and Stochastic Efficacy of Miniature Tensile Specimen Testing

In this article, a miniature dog bone tensile coupon design was tested against the existing ASTM standard specimen design. Specimens were prepared from commercially sourced austenitic stainless steel 304 alloy, and a defect-ridden additively manufactured 304L alloy was studied. By utilizing a tensile specimen design that is 1/230th volume of the smallest ASTM E8-04 (2016), Standard Test Methods for Tension Testing of Metallic Materials, dog bone specimen, coupled to a digital image correlation (DIC) setup, case studies were performed to compare tensile property measurements and strain field evolution. Whereas yield strength measurements were observed to be similar, post-yield, the ultimate strength measurements and ductility measurements from the miniature specimens were observed to be higher than the ASTM specimen design. Although the strength measurements were comparable, the strain evolution was found to differ in the miniature specimens. Studies to assess effects of varying thickness and defect population were also pursued on the miniature tensile specimen. From the DIC strain field estimations, the peak local strain values at ultimate tensile strength were observed to be increasing with reducing specimen thickness. Testing of defect ridden stainless steel revealed the sensitivity to failure through strain localization and the influence of defect size was captured in the strength measurements

Dual-agonist occupancy of orexin receptor 1 and cholecystokinin A receptor heterodimers decreases G-protein-dependent signaling and migration in the human colon cancer cell line HT-29

The orexin (OX1R) and cholecystokinin A (CCK1R) receptors play opposing roles in the migration of the human colon cancer cell line HT-29, and may be involved in the pathogenesis and pathophysiology of cancer cell invasion and metastasis. OX1R and CCK1R belong to family A of the G-protein-coupled receptors (GPCRs), but the detailed mechanisms underlying their functions in solid tumor development remain unclear. In this study, we investigated whether these two receptors heterodimerize, and the results revealed novel signal transduction mechanisms. Bioluminescence and Förster resonance energy transfer, as well as proximity ligation assays, demonstrated that OX1R and CCK1R heterodimerize in HEK293 and HT-29 cells, and that peptides corresponding to transmembrane domain 5 of OX1R impaired heterodimer formation. Stimulation of OX1R and CCK1R heterodimers with both orexin-A and CCK decreased the activation of Gαq, Gαi2, Gα12, and Gα13 and the migration of HT-29 cells in comparison with stimulation with orexin-A or CCK alone, but did not alter GPCR interactions with β-arrestins. These results suggest that OX1R and CCK1R heterodimerization plays an anti-migratory role in human colon cancer cells. [Abstract copyright: Copyright © 2017. Published by Elsevier B.V.