Predictive modeling and experimental analysis of the drilling process of Carbon Fiber Reinforced Polymer composite laminates

Department of Mechanical Enginering (Mechanical Engineering)Carbon fiber reinforced plastic (CFRP) is promising composite material which is a combination of carbon fiber and polymer matrix. CFRP has been actively utilized in the aerospace, automobile, and sport goods due to their superior mechanical properties. High strength-to-weight ratio is one of the biggest advantages of CFRP composite laminates which contribute to development of lighter structural component with abundant strength and stiffness. Although CFRP materials are fabricated in the near-net shape, they still demand the post processing such as drilling, trimming and surface finishing processes to be used as a final product. Among them, the drilling is the most frequently used process for assembly and joining of the components. However, anisotropic and non-homogeneous characteristics of CFRP composite make the drilling process much more difficult than general metallic materials. Above adversities include the reduced life of drill bit with excessive tool wear by materials and leads to defect occurrence in the composite workpiece. Therefore, investigation of optimal process parameters is essential to overcome the adverse effect which can be appeared during drilling process and to make high quality CFRP hole. This study aims at predictive modeling of drilling process of CFRP composite to understand mechanism of force and defect generation. Predictive modeling can be divided into two sections, analytical and numerical studies for a deeper understanding of the process. First, experimental studies were conducted to investigate to figure out the process parameters affecting the drilling process of CFRP composites. Machining process involved with multiple factors, such as machine tool dynamics, tool geometry, material properties, and cutting conditions. Especially, fiber volume fraction, and thermo-mechanical properties of CFRP composite was also considered in the study. Cutting force, and delamination were observed with dynamometer, optical microscope and computational tomography image. It was observed that both thrust force and delamination were intensely related to the feed, and diameter of drill bit. Fiber volume fraction also contribute to the magnitude of cutting force and force changes in time domain. Support plate showed prevention effect of delamination without affecting the cutting force and tool wear. Second, an analytical investigation of the drilling forces for unidirectional CFRP composites according to different machining parameters. Analytical modeling focused on the prediction of thrust force, which was a force generated in the drilling feed direction. Process parameters were selected considering the geometry of drill bit, cutting conditions, and material properties for the accurate calculation. In the analytical modeling in light of drill bit, the chisel edge region is classified as an extrusion operation and the lip is considered as an orthogonal small-element cutting region. The chipping, pressing, and bouncing regions of the lip are incorporated into this thrust force model. In addition, the analytical model includes the thermophysical properties of CFRP by incorporating softening of the material due to heat generation during the cutting processes. Based on the developed model, the thrust force curve for all drilling stages is analyzed in time-domain considering both cutting conditions and material properties. Comparison between predictions and experiments was performed on three CFRP samples (USN 150Y, USN 150B, and USN150E) with different fiber volume fractions and sixteen cutting conditions. It was observed that the predicted forces can capture the trend of the experimental data with the error of the minimum and maximum percentage error for USN 150Y was 12 to 29%, 19 to 36% for USN 150B, and was 16 to 33% for USN 150E. Time-domain analysis and fluctuation evaluation were also used to better understand the mechanism of thrust force during CFRP drilling. Finally, investigation of the delamination of CFRP composites laminates during the drilling process has been conducted. When drilling the CFRP laminates, delamination is one of the severe defects that degrade the quality of CFRP products. However, delamination is the damage propagation inside the material which demand numerical analysis to observe stress-strain behavior. Therefore, finite element model of CFRP drilling was developed using commercial FE software to simulate the damages generated in the cohesive zone between the interface of laminates. Drilling simulation and tests were conducted to analyze effect of feed, spindle speed, and back-up plate on delamination as well as the thrust force. Before the delamination analysis, calculated thrust force results were validated by the experimental results to assure the accuracy of simulation. Delamination of the FE model were assessed by the evaluating damage criterion value of cohesive zone between the composite laminate plies. Quantified delamination factors of simulation were compared to the experimental results which acquired and processed by computational tomography image and image processing techniques and showed good agreement.ope

Algorithmic Trading and Directors’ Learning from Stock Prices: Evidence from CEO Turnover Decisions

We examine the effect of algorithmic trading (AT) on directors’ learning from stock prices. We find that the sensitivity of forced CEO turnover to stock returns decreases with AT. We mitigate correlated omitted variable bias by using the 2016 Tick Size Pilot Program as an exogenous shock to AT. In cross-sectional analyses, we document that the negative effect of AT is more pronounced for growth firms, firms with greater exposure to macroeconomic factors, and firms with a geographically dispersed investor base, where the information that AT crowds out is more likely to be new to directors. We also find that the effect is stronger when directors’ expertise likely allows them to extract decision-relevant information from prices and when the directors’ own information set is poor. Overall, our findings suggest that stock prices aggregate information about CEO performance and CEO-firm match, which is otherwise unavailable to directors, and that directors incorporate this information into their CEO turnover decisions

ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity

Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents widespread clinical application. Visual language pre-training (VLP) can alleviate the burden and cost of annotation by leveraging routinely generated reports for radiographs, which exist in large quantities as well as in paired form (imagetext pairs). Additionally, extensions to localization-aware VLPs are being proposed to address the needs of accurate localization of abnormalities for CAD in CXR. However, we find that the formulation proposed by locality-aware VLP literatures actually leads to loss in spatial relationships required for downstream localization tasks. Therefore, we propose Empowering Locality of VLP with Intra-modal Similarity, ELVIS, a VLP aware of intra-modal locality, to better preserve the locality within radiographs or reports, which enhances the ability to comprehend location references in text reports. Our locality-aware VLP method significantly outperforms state-of-the art baselines in multiple segmentation tasks and the MS-CXR phrase grounding task. Qualitatively, ELVIS is able to focus well on regions of interest described in the report text compared to prior approaches, allowing for enhanced interpretability.Comment: Under revie

Vertical-Type Organic Light-Emitting Transistors with High Effective Aperture Ratios

The inherent complexity of the structures of active-matrix (AM) organic light-emitting diode (OLED) displays severely limits not only their size but also device performance. Surface-emitting organic light-emitting transistors (OLETs) may offer an attractive alternative to AM displays. We report some characteristics of vertical-type OLETs (VOLETs) composed of a source electrode of low-dimensional materials and an emissive channel layer. With a functionalized graphene source, it is shown that the full-surface electroluminescent emission of a VOLET can be effectively controlled by the gate voltage with a high luminance on/off ratio (104). The current efficiency and effective aperture ratios were observed to be more than 150% of those of a control OLED, even at high luminances exceeding 500 cd m−2. Moreover, high device performance of micro-VOLET pixels has been also successfully demonstrated using inkjet-patterned emissive channel layers. These significant improvements in the device performance were attributed to the effective gate-voltage-induced modulation of the hole tunneling injection at the source electrode

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region

paraFaceTest: an ensemble of regression tree-based facial features extraction for efficient facial paralysis classification

Abstract Background Facial paralysis (FP) is a neuromotor dysfunction that losses voluntary muscles movement in one side of the human face. As the face is the basic means of social interactions and emotional expressions among humans, individuals afflicted can often be introverted and may develop psychological distress, which can be even more severe than the physical disability. This paper addresses the problem of objective facial paralysis evaluation. Methods We present a novel approach for objective facial paralysis evaluation and classification, which is crucial for deciding the medical treatment scheme. For FP classification, in particular, we proposed a method based on the ensemble of regression trees to efficiently extract facial salient points and detect iris or sclera boundaries. We also employ 2 n d degree polynomial of parabolic function to improve Daugman’s algorithm for detecting occluded iris boundaries, thereby allowing us to efficiently get the area of the iris. The symmetry score of each face is measured by calculating the ratio of both iris area and the distances between the key points in both sides of the face. We build a model by employing hybrid classifier that discriminates healthy from unhealthy subjects and performs FP classification. Results Objective analysis was conducted to evaluate the performance of the proposed method. As we explore the effect of data augmentation using publicly available datasets of facial expressions, experiments reveal that the proposed approach demonstrates efficiency. Conclusions Extraction of iris and facial salient points on images based on ensemble of regression trees along with our hybrid classifier (classification tree plus regularized logistic regression) provides a more improved way of addressing FP classification problem. It addresses the common limiting factor introduced in the previous works, i.e. having the greater sensitivity to subjects exposed to peculiar facial images, whereby improper identification of initial evolving curve for facial feature segmentation results to inaccurate facial feature extraction. Leveraging ensemble of regression trees provides accurate salient points extraction, which is crucial for revealing the significant difference between the healthy and the palsy side when performing different facial expressions

Engineering Tools for the Development of Recombinant Lactic Acid Bacteria

Lactic acid bacteria (LAB) is mainly used in food fermentation. In addition, LAB fermentation technology has been studied in the development of industrial food additives, nutrients, or enzymes used in food processing. In the field of red biotechnology, LAB is approved and is generally recognized as a safe organism and is considered safe for biotherapeutic treatments. Recent clinical trials have demonstrated the medicinal value of therapeutic recombinant LAB and the suitability of innate mechanisms of secretion and anchoring for therapeutic applications such as antibody or vaccine production. However, the gram-positive phenotypic trait of LAB creates challenges for genetic modifications when compared to other conventional workhorse bacteria, resulting in exclusive developments of genetic tools for engineering LAB. In this review, several distinct approaches in gene expression for engineering LAB are discussed.N

Dynamic performance of industrial robots in the secondary carbon fiber-reinforced plastics machining

Carbon fiber-reinforced plastics (CFRPs) find many applications given their superior properties. These materials are usually formed using a near-net-shape method that requires secondary machining, such as drilling and trimming, after molding. Industrial robots are becoming increasingly popular machining tools in industries exhibiting high demand for CFRPs. However, it remains challenging to achieve high dimensional accuracy when using such robots and dynamic performance is poor. We experimentally investigated the dynamic properties of the tool tip according to the dominant robot posture during CFRP secondary machining. Based on the results, multi-layer perceptron models were developed to predict the dominant natural frequency and dynamic stiffness of the tool tip. The minimum and maximum mean absolute percentage errors were 1.99 and 7.94, respectively; the error changed markedly with robot posture. Our models improved CFRP robotic machinability. Experimentally, the delamination rates of drilled holes decreased by 15 % and 75 % in terms of length and area, respectively, and the trimmed surface roughness improved by 27 %