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

Additive Manufacturing and Performance of Architectured Cement-Based Materials

There is an increasing interest in hierarchical design and Additive Manufacturing (AM)of cement-based materials. However, the brittle behavior of these materials and the presence of interfaces from the additive manufacturing process represent the current major challenges. Our work focuses on harnessing the heterogeneous interfaces by employing clever designs from bio-inspired Bouligand architectured materials. In this paper, we aim to demonstrate some key mechanisms that can allow brittle hardened cement-based materials to gain flaw-tolerant properties. Mechanisms such as cracktwisting at the interfaces have been previously observed in naturally-occurring orsynthetic composite Bouligand architectures. In this paper, a heterogeneous interface with porous characteristics in 3D-printed solid hardened cement paste (hcp)architectures were characterized. We hypothesize that the presence of heterogeneous interface in 3D-printed hardened cement paste (hcp) elements, in conjunction with clever architectures, promote key damage mechanisms such as interfacial cracking and crack twisting that lead to damage delocalization. This delocalization can be energetically favorable and allow energy dissipation and promote toughening and flaw-tolerant properties. We found that these architectures can enhance the properties from the typical strength-porosity relationship, classically known for brittle hcp materials

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance