Micromechanical modeling of the machining behavior of natural fiber-reinforced polymer composites

This paper aims to develop a 2D finite element (FE) model at microscale for numerical simulation of the machining behavior of natural fiber-reinforced polymer (NFRP) composites. The main objective of this study is to reproduce the experimentally observed specific cutting behavior of natural fibers within the composite material. Flax fiber-reinforced polypropylene (PP) composites are modeled separately using an elasto-plastic behavior with a ductile damage criterion for flax fibers and PP matrix, while the microscopic interfaces are represented using the cohesive zone modeling (CZM). Numerical outputs are compared with experimental results for the FE model validation. Results show that the proposed FE model can reproduce the cutting force with a good precision for a large cutting speed range (12–80 m/min). The FE model shows also an efficiency and accuracy in predicting the cutting behavior of flax fibers by reproducing the fiber deformation, the fibers torn-off, and the fracture of the interfaces during machining. Moreover, the FE model can be an effective tool for analyzing the quality of the microscopic interfaces in the NFRP composites after machining

Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals

Background: Vectorcardiogram (VCG) signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns.Materials and methods: We present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG) through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility.Results: In this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC), myocardial infarction (MI), atrial fibrillation (AF) and bundle branch block (BBB). The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80) of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368) of MI subjects are found to remain in Octant 6 and the rest (68.21%) spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more.Conclusions: Spatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac pathological patterns and enhance the automatic assessment of cardiovascular diseases.Peer reviewedIndustrial Engineering and ManagementMechanical and Aerospace Engineerin

Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization

The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent hierarchical DP clustering model for identification of specific process anomalies. The presented DPGM models are validated using numerical simulation studies as well as wireless vibration signals acquired from an experimental semiconductor chemical mechanical planarization (CMP) test bed. Through these numerically simulated and experimental sensor data, we test the hypotheses that DPGM models have significantly lower detection delays compared with SPC charts in terms of the average run length (ARL1) and higher defect identification accuracies (F-score) than popular clustering techniques, such as mean shift. For instance, the DP-based SPC chart detects pad wear anomaly in CMP within 50 ms, as opposed to over 140 ms with conventional control charts. Likewise, DPGM models are able to classify different anomalies in CMP

Quantification of Ultraprecision Surface Morphology using an Algebraic Graph Theoretic Approach

Assessment of progressive, nano-scale variation of surface morphology during ultraprecision manufacturing processes, such as fine-abrasive polishing of semiconductor wafers, is a challenging proposition owing to limitations with traditional surface quantifiers. We present an algebraic graph theoretic approach that uses graph topological invariants for quantification of ultraprecision surface morphology. The graph theoretic approach captures heterogeneous multi-scaled aspects of surface morphology from optical micrographs, and is therefore valuable for in situ real-time assessment of surface quality. Extensive experimental investigations with specular finished (Sa ~ 5 nm) blanket copper wafers from a chemical mechanical planarization (CMP) process suggest that the proposed method was able to quantify and track variations in surface morphology more effectively than statistical quantifiers reported in literature

Correction: Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.

[This corrects the article DOI: 10.1371/journal.pone.0164406.]

A Dirichlet Process Gaussian State Machine Model for Change Detection in Transient Processes

<p>The ability to detect incipient and critical changes in real world process—esessential for system integrity assurance—is currently impeded by the mismatch between the key assumption of stationarity underlying most change detection methods and the nonlinear and nonstationary (transient) dynamics of most real-world processes. The current approaches are slow or outright unable to detect qualitative changes in the behaviors that lead to anomalies. We present a Dirichlet process Gaussian state machine (DPGSM) model to represent dynamic intermittency, which is one of the most ubiquitous real-world transient behaviors. The DPGSM model treats a signal as a random walk among a Dirichlet process mixture of Gaussian clusters. Hypothesis tests and a numerical scheme based on this nonparametric representation were developed to detect subtle changes in the transient (intermittent) dynamics. Experimental investigations suggest that the DPGSM approach can consistently detect incipient, critical changes in intermittent signals some 50–2000 ms (20–90%) ahead of competing methods in benchmark test cases as well as a variety of real-world applications, such as in alternation patterns (e.g., ragas) in a music piece, and in the vibration signals capturing the initiation of product defects in an ultraprecision manufacturing process. A supplementary file to this article, available online, includes a Matlab implementation of the presented DPGSM.</p

Data from: Nonlinear dynamics forecasting of obstructive sleep apnea onsets

Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders