Defective Parts Research Articles

Carbon-emission and waste reduction of a manufacturing-remanufacturing system using green technology and autonomated inspection

Environmental-friendly technology helps to reduce waste and carbon emissions of an imperfect production system. In general, the defective products generated during the “out-of-control” state are treated as waste. The single-stage manufacturing-remanufacturing system effectively depletes such defective spare parts within the same cycle but causes a tremendous amount of carbon. In such a circumstance, green technology to reduce carbon emissions is highly recommended. Also, the autonomated inspection makes defective detection more reliable and is ultimately helpful for waste reduction. Hence, in this study, we optimize the production plan along with the investments for applying green technology and autonomated inspection in an assembled product manufacturing-remanufacturing system. The numerical result shows that the appropriate green technology decreases carbon emissions up to 2.81% and autonomated inspection reduces the waste up to 2.37%, along with a reduction of entire production cycle cost up to 18.26%. In addition, the setup cost reduction is considered due to the characteristics of assembled product production.

Crucial interfacial shear strength to consider an imperfect interphase in halloysite-nanotube-filled biomedical samples

“τc” is defined as crucial interfacial shear strength for effectual shifting of stress through a deficient interphase and its role in the strength of nanocomposites filled with halloysite nanotubes (HNT) is highlighted by progressing the Kolarik model. The developed model assumes mechanical percolation, defective interphase part, and filler/interphase nets. The outputs of the model suitably follow the experimented levels of several samples. The strength of the nanocomposite improves by 15% and 45% at network strengths of 2 and 6 GPa, respectively. Additionally, the crucial interfacial shear strength of 10 and 150 MPa increase the strength of samples by 80% and 10%, respectively. Moreover, the strength of the nanocomposite improves by 18% at an interfacial shear strength of 40 MPa, while the interfacial shear strength of 80 MPa improves the strength of the system by 34%. Furthermore, the strength of samples enhances by 51% at the beginning of percolation at 0.001; however, the strength of the nanocomposite only grows by 25% at the beginning of percolation at 0.01. Accordingly, both network strength and interfacial shear strength directly govern the strength of the nanocomposite, while the crucial interfacial shear strength and the beginning of percolation adversely affect the strengthening effect.

NONLINEAR STATIONARY OSCILLATIONS OF BEAM GIRDER STRUCTURES FROM THE ACTION OF PERIODIC PULSES

Purpose. The aim of this work is to construct an approximate solution of stationary oscillations of a nonlinear equation with a cubic characteristic. The design scheme under consideration best corresponds to the operating conditions of the girder structure in the event of defects of the supporting parts. In this case, longitudinal forces may appear in the girder of the girder structure. Methods. The paper investigates nonlinear oscillations of girder structures of small bridges. The undamped oscillations of the beam resting on hinged fixed support parts on both sides from the action of periodic unilateral impulse loading are considered. The design scheme differs from the statically defined beam scheme, which takes into account defective support parts. In the article, the possible transition to the beam system on moving support parts is considered. To solve the nonlinear equation of the second order of a single-span beam taking into account the longitudinal force, a method of variable time scale used, developed by Academician of the Academy of Sciences of the USSR, professor Bondar M. G. The method of variable scale gives fairly approximate solutions to this class of problems. Results. An approximate analytical solution is performed by the method of variable time scale of stationary nonlinear oscillations of the girder structure taking into account the longitudinal force caused by one-sided pulses that are periodically repeated. According to the obtained formulas of the solution of the nonlinear equation with cubic characteristic it is easy to construct amplitude-frequency characteristics and forms of oscillations. Originality. For the first time the solution of the equation of oscillations of a beam taking into account longitudinal force is executed. The criteria of impact resonance for the system without friction, as well as taking into account the viscous friction. An expression for the amplitude-frequency characteristic of the system under consideration is obtained. Practical meaning. Studies of beam structure oscillations taking into account the longitudinal force gave practical value in the possibility of analytical calculation of oscillation forms, shock resonance criteria and amplitude-frequency characteristics of the system from the action of periodic unilateral impulse loads. The system of girder beams takes into account defective supporting parts (jamming of movable supporting parts) of girder structures of small and medium railway bridgesd.

Real-space representation of the quasiparticle self-consistent GW self-energy and its application to defect calculations

The quasiparticle self-consistent QS$GW$ approach incorporates the corrections of the quasiparticle energies from their Kohn-Sham density functional theory (DFT) eigenvalues by means of an energy independent and Hermitian self-energy matrix usually given in the basis set of the DFT eigenstates. By expanding these into an atom-centered basis set (specifically here the linearized muffin-tin orbitals) a real space representation of the self-energy corrections becomes possible. We show that this representation is relatively short-ranged. This offers new opportunities to construct the self-energy of a complex system from parts of the system by a cut-and-paste method. Specifically for a point defect, represented in a large supercell, the self-eneregy can be constructed from those of the host and a smaller defect containing cell. The self-energy of the periodic host can be constructed simply from a $GW$ calculation for the primitive cell. We show for the case of the As$_\mathrm{Ga}$ in GaAs that the defect part can already be well represented by a minimal 8 atom cell and allows us to construct the self-energy for a 64 cell in good agreement with direct QS$GW$ calculations for the large cell. Using this approach to an even larger 216 atom cell shows the defect band approaches an isolated defect level. The calculations also allow to identify a second defect band which appears as a resonance near the conduction band minimum. The results on the extracted defect levels agree well with Green's function calculations for an isolated defect and with experimental data.

Targeting Stress Sensor Kinases in Hepatocellular Carcinoma-Infiltrating Human NK Cells as a Novel Immunotherapeutic Strategy for Liver Cancer.

Natural killer (NK) cells may become functionally exhausted entering hepatocellular carcinoma (HCC), and this has been associated with tumor progression and poor clinical outcome. Hypoxia, low nutrients, immunosuppressive cells, and soluble mediators characterize the intratumor microenvironment responsible for the metabolic deregulation of infiltrating immune cells such as NK cells. HCC-infiltrating NK cells from patients undergoing liver resection for HCC were sorted, and genome-wide transcriptome profiling was performed. We have identified a marked general upregulation of gene expression profile along with metabolic impairment of glycolysis, OXPHOS, and autophagy as well as functional defects of NK cells. Targeting p38 kinase, a stress-responsive mitogen-activated protein kinase, we could positively modify the metabolic profile of NK cells with functional restoration in terms of TNF-α production and cytotoxicity. We found a metabolic and functional derangement of HCC-infiltrating NK cells that is part of the immune defects associated with tumor progression and recurrence. NK cell exhaustion due to the hostile tumor microenvironment may be restored with p38 inhibitors with a selective mechanism that is specific for tumor-infiltrating—not affecting liver-infiltrating—NK cells. These results may represent the basis for the development of a new immunotherapeutic strategy to integrate and improve the available treatments for HCC.

In situ process quality monitoring and defect detection for direct metal laser melting

Quality control and quality assurance are challenges in direct metal laser melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that leverage existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. In one methodology, a Bayesian approach attributes measurements to one of multiple process states as a means of classifying process deviations. In a second approach, a least squares regression model predicts severity of certain material defects.

Multi-mode laser-ultrasound imaging using Time-domain Synthetic Aperture Focusing Technique (T-SAFT)

The Synthetic Aperture Focusing Technique (SAFT) is an imaging algorithm used in laser ultrasonics (LU) to visualise the appearance of defects. However, ultrasound excited by a pulsed laser has the characteristics of wide bandwidth and multi-mode directivity patterns, leading to common problems in the SAFT process, such as low utilisation of ultrasound information and possible artefacts. To solve these problems, a Multi-mode Time-domain SAFT (MMT-SAFT) algorithm is proposed in this paper. The influence of ultrasonic directivity is discussed according to the imaging depth range, and imaging with multiple LU modes is performed to reduce artefacts. Simulations and experimental results prove the feasibility of the MMT-SAFT algorithm, which not only presents a clearer image of the upper part of defects but also improves image quality compared with time-domain SAFT using a single ultrasonic mode. The proposed technique can provide feasible directions for laser ultrasonic defect imaging.

Diagnosing and treating Patent Foramen Ovale (PFO) from various manifestations in adults: case series

Background. Patent foramen ovale (PFO) is a part of atrial septal defects (ASD) entities. While mostly asymptomatic, some adults with PFO will develop symptoms, from mild complaints such as migraine or palpitation to more debilitating complications such as syncope or cryptogenic stroke. Therefore, it is highly crucial that PFO be diagnosed and treated promptly. Echocardiography, trans-thoracal and or trans-esophageal, is the main diagnosis modality. For treatment, antiplatelet with or without anticoagulation, trans-catheter closure, and surgery are utilized in accordance with their indications. Case Presentation. We present four adult patients referred with various symptoms: vertigo, migraine, palpitation, and cryptogenic stroke. Three of them had no explicit risk factors for the cerebrovascular event, with one patient having a co-existing hypercoagulable state. Echocardiography revealed a positive bubble test, which meant that PFO was the primary etiology of those cases. We then treated each of them with transcatheter device closure, which improved their symptoms, before adding antiplatelet for six months. Conclusion. Proper diagnosis and prompt treatment of PFO are imperative in reducing complications. Echocardiography, mainly TEE, is the main diagnostic tool for PFO. Antithrombotic (with or without anticoagulant), minimally invasive procedure and surgery in some cases are the modalities available.

Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles

Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.

Process Monitoring and Estimation of Material Properties of Additively Manufactured Components Using Model-Based Inversion of Process Compensated Resonance Testing Data

Abstract Certification of additively manufactured (AM) parts and qualification of AM processes, suppliers, and machines for aerospace applications involve significant mechanical testing, cost, and time. The AM community requires capabilities for fast, affordable, and effective certification and qualification. These capabilities include rapid component and model validation, characterization of process and geometry impact on material properties, build and process monitoring, and effective nondestructive evaluation (NDE). Process compensated resonance testing (PCRT) uses the resonance frequencies of a component to rapidly detect defective parts, monitor build and processes and post-processing, characterize material properties, and can be used as a basis for model validation. This study examines the use of PCRT model-based inversion to nondestructively determine the material properties of as-built titanium AM components. Over 100 samples were printed with intentional variations in build parameters, with the intention to produce generic “acceptable” and “unacceptable” components. PCRT modeling tools were then used to train the inversion code algorithms. The model-based inversion estimated the elastic properties of the as-built parts as well as a residual fit error, identifying several components with outlying properties. Parts were then mechanically tested, and the outliers were confirmed. As PCRT model-inversion is a powerful NDE method, several examples are described showing how inversion results can augment traditional AM inspection techniques.

Optical Film Damage Classification Based on Neural Network

Traditional machine learning requires users to have a strong ability to control features and distance calculation formulas, especially in the use of support vector machine SVM and nearest neighbor KNN. Traditional machine learning uses PCA in feature extraction will actually lead to Information is lost. In order to solve the problem of low optical film damage detection rate of traditional methods, a new method is proposed in this paper based on a convolutional neural network instead of traditional machine learning to classify CCD images with different damage degrees of SiO2 film and K9 glass. First, film images are collected by online CCD, and the proposed algorithm is designed to extract the image characteristic parameters of the film microscopic images, filter denoising, and run binarization to analyze film images. Second, gray values of images are extracted and classified by unsupervised learning. Finally, the film microscopic images under the microscope are analyzed. The experimental results show that the defect positions on the images can be detected after the images are detected and processed by a convolution neural network, binarization, and connected domains. The defective parts can be intercepted from the images, and the data related is saved for damage type determination. The average classification rate is over 99%, which is better than the traditional method by 9.1%. Therefore, it has a high application value.

Non-submerged reconstructive approach for peri-implantitis osseous defect with removal of implant crowns: One-year outcomes of a prospective case series study.

The aim of this study was to test a non-submerged reconstructive approach for peri-implantitis osseous defects, by removing the prosthetic components, augmenting of the infraosseous bony compartment, and flap readaptation around the replaced healing abutments, without obtaining a primary wound closure. Twenty-nine implants in 24 patients were treated. Implant suprastructures were removed at the time of the intervention, to aid with the debridement process which included curettage, implantoplasty, air-power driven devices, and locally delivered antibiotics. The infraosseous part of peri-implant defects were augmented using a composite bone graft and an absorbable membrane to be secured around the replaced healing abutments without attempting to submerge the implants. After 8 months, direct peri-implant defect measurements were obtained to serve as the primary outcome. Secondary outcomes included of radiographic bone changes, and probing depth (PD) and bleeding on probing (BOP) changes at 12 months. At the time of the surgical re-entry (8 months), a statistically significant clinical and radiographic defect fill was observed (average of 2.33 and 1.63mm, respectively). Approximately 3 months after crown replacement, 12 months from the surgical intervention, a significant PD (1.51mm) and BOP (65%) reduction were also noted. Considering its limitations, the use of a non-submerged approach (with removal of implant crowns) led to significant improvements in clinical (defect fill, PD, BOP) and radiographic outcomes.

Comparative study on risk of birth defects in singleton ART birth under high levels of estrogen after fresh embryo transfer and frozen embryo transfer

Objective To investigate whether high estrogen (E2) levels caused by controlled ovarian hyperstimulation affect the birth defect rate in singleton assisted reproductive technology (ART) birth after conceived by fresh embryo transfer and frozen embryo transfer (FET). Methods This was a retrospective cohort study. A total of 581 women with singletons, as well as those who have become pregnant and have had an unwanted abortion under high E2 levels on trigger day were divided into three groups. Group A received FET and the E2 levels on trigger day were higher than 5000 pg/ml. Group B received fresh embryo transfer and the E2 levels were between 3000 and 5000 pg/ml. Group C received FET and the E2 levels were between 3000 and 5000 pg/ml. Results There were no significant differences in birth weight, delivery mode, preterm birth rate, and fetal sex between the three groups (p > .05). Birth defect rate in Group B was higher than that in Group A and C, and the rate between Group B and C had significant differences (p < .05). After adjusting for maternal age, BMI, and type of infertility, only a FET cycle is significantly associated with decreased birth defect rate. Conclusion Fresh embryo transfer under supraphysiological level of estrogen exposure may increase the birth defect rate of ART singletons. Even after prenatal screening and diagnosis, a part of birth defect could not be detected during pregnancy. When the estrogen levels on trigger day were no lower than 3000 pg/ml, FET should be advocated to reduce the occurrence of such risk.

Electronic transport properties of a-Si:H

To investigate the electron transport properties of hydrogenated amorphous silicon (a-Si:H), a series of quantum simulations and electron transport analyses were performed. The target system is a nano-scale junction of a-Si:H with various hydrogen concentrations sandwiched between two metal electrodes. The density functional based tight binding simulation was conducted to obtain the electronic structure, and the non-equilibrium Green’s function method was adopted to evaluate the electron transmission coefficient and the electric current under a bias field. It is confirmed that the hydrogen atoms passivate a part of defects in amorphous silicon, but the remaining defects realize the energy states in the bandgap; the p orbitals of silicon atoms mainly contribute to the electron transmission. The transport behavior is greatly affected by the hydrogen concentration. The interface between a-Si:H and the metal electrodes also influences the transport behavior through changing the spatial charge density inside the a-Si:H.

Automated defect detection in digital radiography of aerospace welds using deep learning

Aerospace welds are non-destructively evaluated (NDE) during manufacturing to identify defective parts that may pose structural risks, often using digital radiography. The analysis of these digital radiographs is time consuming and costly. Attempts to automate the analysis using conventional computer vision methods or shallow machine learning have not, thus far, provided performance equivalent to human inspectors due to the high reliability requirements and low contrast to noise ratio of the defects. Modern approaches based on deep learning have made considerable progress towards reliable automated analysis. However, limited data sets render current machine learning solutions insufficient for industrial use. Moreover, industrial acceptance would require performance demonstration using standard metrics in non-destructive evaluation, such as probability of detection (POD), which are not commonly used in previous studies. In this study, data augmentation with virtual flaws was used to overcome data scarcity, and compared with conventional data augmentation. A semantic segmentation network was trained to find defects from computed radiography data of aerospace welds. Standard evaluation metrics in non-destructive testing were adopted for the comparison. Finally, the network was deployed as an inspector’s aid in a realistic environment to predict flaws from production radiographs. The network achieved high detection reliability and defect sizing performance, and an acceptable false call rate. Virtual flaw augmentation was found to significantly improve performance, especially for limited data set sizes, and for underrepresented flaw types even at large data sets. The deployed prototype was found to be easy to use indicating readiness for industry adoption.

Enabling efficient sub-block disabled caches using coarse grain spatial predictions

Reducing the supply voltage in today′s process technologies introduces significant reliability challenges for on-chip SRAM arrays. As a reaction, many Cache Fault-Tolerance (CFT) techniques have been developed to ensure error free and performance efficient execution in the presence of faults. A class of recent CFT techniques are based on the concept of disabling cache portions, such as (sub-)blocks or words that include defective bits, and reconfiguring operational ones (e.g., physical or logical neighbor (sub-)blocks). All those techniques require significant circuit modifications which may pose unacceptable overheads to time-sensitive L1 caches. In this work we propose to deal with the cache reliability problem at a very different level. We assume that faulty caches are enhanced with the ability of disabling their defective parts at different sub-block granularities. The key idea is to leverage the spatial reuse patterns of the memory blocks (not all the data fetched into the cache is accessed) in order to mask out the additional, due-to-faults, cache misses. To this end, we propose a new class of frugal spatial predictors (with 24-bytes storage requirements) to orchestrate the replacement decisions of the fully and partially functional cache blocks. Our evaluation results reveal that the proposed approach is able to offer 32.3% on average, among all studied percentage of failures, increase in hit ratio in first level data caches (64.6% in instruction caches) over a conventional faulty cache (based on block disabling). Further, experimental results reveal the superiority of the proposed method with respect to Instructions-Per-Cycle and Energy-Delay-Product.

Surface defect detection of Si3N4 ceramic bearing ball based on improved homomorphic filter-Gaussian filter coupling algorithm

Considering the tiny surface defects of Si3N4 ceramic bearing balls and the low accuracy of defect detection using a single traditional algorithm, we find that the performance of the aerospace mechanical power system is poor. A coupling algorithm based on the improved homomorphic filter and Gaussian filter is proposed. The Si3N4 ceramic bearing ball nondestructive testing platform is established, by which the surface defect images of Si3N4 ceramic bearing balls are collected. The image acquisition card obtains the image information and then transmits it to the image storage module. The image processing module handles surface defect images. The gray transformation algorithm is used to complete image preprocessing. Gaussian noise in images is filtered out using the Gaussian filter algorithm. The homomorphic filtering algorithm is used to enhance the high frequency component, compress the low frequency component, and filter out the convolution noise and promiscuous signal. The contrast of the defect part has been reinforced using the coupling algorithm. It turns out that the accuracy of the coupling algorithm is 100%, 96.7%, 98.9%, and 94.4%.

Porous carbon textile decorated with VC/V2O3-X hybrid nanoparticles: Dual-functional host for flexible Li-S full batteries

The polysulfide shuttling at the cathode and lithium dendrite formation at the anode remain as major obstacles to overcome for the realization of high-performance lithium-sulfur (Li-S) batteries. Here, via a facile one-pot synthesis using carbothermic reduction, we prepared a porous and flexible carbon textile uniformly decorated with VC/V2O3-x hybrid nanoparticles, which was used as a robust and flexible host material for both sulfur cathode and Li metal anode. We observed that the defective V2O3-x part traps polysulfides and the conductive VC part catalytically enhances the fragmentation of the polysulfides. Moreover, it was found that the well-distributed VC/V2O3-x nanoparticles act as lithiophilic sites to lead uniform lithium nucleation for a dendrite-free lithium metal anode. The full battery delivered superb rate performance (882 mAh g − 1 at 5 C) and an ultralow decay rate of 0.02% per cycle during 1000 cycles at 1 C. Even with a large sulfur loading of 7.0 mg cm−2, a high areal capacity of 6.29 mAh cm−2 at 0.2 C was obtained. This work provides an effective remedy to simultaneously resolve problems regarding the shuttle effect and the Li dendrite formation for the realization of high-performance flexible Li-S full batteries.

Effect of Defects Part I: Degradation of Constitutive Coefficients as an Input to the Composite Failure Model with Microvoids and Porosity

It is always challenging to provide appropriate material properties for a composite progressive failure model. The nonstandard percentage reduction method that is commonly used to degrade the material constants with micro-scale defects generates tremendous uncertainty in failure prediction. The constitutive matrix is composed of multiple material constants. It is not necessary that all constants degrade either equally or linearly due to a certain state of material defects. With this very concern in mind, this article presents a guideline for using a quantified perturbation for each coefficient appropriately. It also presents distribution of effective material properties (EMPs) in unidirectional composite materials with different states of defects such as voids. Irrespective of resin transfer molding (RTM) or chemical vapor infiltration (CVI) processes, manufacturers’ defects such as voids of different shapes and sizes are the most common that occur in composite materials. Hence, it is important to quantify the ‘effects of defects’ void content herein on each material coefficient and EMP. In this article, stochastically distributed void parameters such as the void content by percent, size, shape, and location are considered. Void diameters and shapes were extracted from scanning acoustic microscope (SAM) images of 300,000 cycles of a fatigued composite. The EMPs were calculated by considering unit cells, homogenization techniques, and micromechanical concepts. The periodic boundary conditions were applied to unit cells to calculate the EMPs. The result showed that EMPs were degraded even when there was a small percentage of the void content. More importantly, the constitutive coefficients did not degrade equally but had a definitive pattern.

Detection of Internal Defects in Concrete and Evaluation of a Healthy Part of Concrete by Noncontact Acoustic Inspection Using Normalized Spectral Entropy and Normalized SSE.

Non-destructive testing, with non-contact from a remote location, to detect and visualize internal defects in composite materials such as a concrete is desired. Therefore, a noncontact acoustic inspection method has been studied. In this method, the measurement surface is forced to vibrate by powerful aerial sound waves from a remote sound source, and the vibration state is measured by a laser Doppler vibrometer. The distribution of acoustic feature quantities (spectral entropy and vibrational energy ratio) is analyzed to statistically identify and evaluate healthy parts of concrete. If healthy parts in the measuring plane can be identified, the other part is considered to be internal defects or an abnormal measurement point. As a result, internal defects are detected. Spectral entropy (SE) was used to distinguish between defective parts and healthy parts. Furthermore, in order to distinguish between the resonance of a laser head and the resonance of the defective part of the concrete, spatial spectral entropy (SSE) was also used. SSE is an extension of the concept of SE to a two-dimensional measuring space. That is, based on the concept of SE, SSE is calculated, at each frequency, for spatial distribution of vibration velocity spectrum in the measuring plane. However, these two entropy values were used in unnormalized expressions. Therefore, although relative evaluation within the same measurement surface was possible, there was the issue that changes in the entropy value could not be evaluated in a unified manner in measurements under different conditions and environments. Therefore, this study verified whether it is possible to perform a unified evaluation for different defective parts of concrete specimen by using normalized SE and normalized SSE. From the experimental results using cavity defects and peeling defects, the detection and visualization of internal defects in concrete can be effectively carried out by the following two analysis methods. The first is using both the normalized SE and the evaluation of a healthy part of concrete. The second is the normalized SSE analysis that detects resonance frequency band of internal defects.