Search results for: machining accuracy.

Authors: F. Haghjoo, M. Sarlak, S.M. Shahrtash

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In order to perform on-line measuring and detection of PD signals, a total solution composing of an HFCT, A/D converter and a complete software package is proposed. The software package includes compensation of HFCT contribution, filtering and noise reduction using wavelet transform and soft calibration routines. The results have shown good performance and high accuracy.

Keywords: Partial Discharge, Measurement, On-line, HFCT

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Authors: Wen-Yen Wu, Wen-Bin Yu

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In this paper, we propose a method for detecting circular shapes with subpixel accuracy. First, the geometric properties of circles have been used to find the diameters as well as the circumference pixels. The center and radius are then estimated by the circumference pixels. Both synthetic and real images have been tested by the proposed method. The experimental results show that the new method is efficient.

Keywords: Subpixel, least squares estimation, circle detection, Hough transformation.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as physiological vital signs, images and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful to influence the judgement of clinical sentiment in ICU clinical notes. This paper presents two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of a clinical transformer model that can reliably predict clinical sentiment for notes of COVID patients in ICU. We train the model on clinical notes for COVID-19 patients, ones not previously seen by Bio_ClinicalBERT or Bio_Discharge_Summary_BERT. The model which was based on Bio_ClinicalBERT achieves higher predictive accuracy than the one based on Bio_Discharge_Summary_BERT (Acc 93.33%, AUC 0.98, and Precision 0.96). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and Precision 0.92).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation.

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Authors: S. D. El Wakil

Abstract:

The research work reported here was aimed at investigating the feasibility of joining high-porosity stainless steel discs and wrought iron bars by friction welding. The sound friction-welded joints were then subjected to a metallurgical investigation and an analysis of failure resulting from tensile loading. Discs having 50 mm diameter and 10 mm thickness were produced by loose sintering of stainless steel powder at a temperature of 1350 ^oC in an argon atmosphere for one hour. Minor machining was then carried out to control the dimensions of the discs, and the density of each disc could then be determined. The level of porosity was calculated and was found to be about 40% in all of those discs. Solid wrought iron bars were also machined to facilitate tensile testing of the joints produced by friction welding. Using our previously gained experience, the porous stainless steel disc and the wrought iron tube were successfully friction welded. SEM was employed to examine the fracture surface after a tensile test of the joint in order to determine the type of failure. It revealed that the failure did not occur in the joint, but rather in the in the porous metal in the area adjacent to the joint. The load carrying capacity was actually determined by the strength of the porous metal and not by that of the welded joint. Macroscopic and microscopic metallographic examinations were also performed and showed that the welded joint involved a dense heat-affected zone where the porous metal underwent densification at elevated temperature, explaining and supporting the findings of the SEM study.

Keywords: Fracture of friction-welded joints, metallurgy of friction welding, solid-porous structures, strength of joint.

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Authors: Taposh Roy, Anna Paradowska, Ralph Abrahams, Quan Lai, Michael Law, Peter Mutton, Mehdi Soodi, Wenyi Yan

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In this investigation, a laser cladding process with a powder feeding was used to deposit stainless steel 410L (high strength, excellent resistance to abrasion and corrosion, and great laser compatibility) onto railhead (higher strength, heat treated hypereutectoid rail grade manufactured in accordance with the requirements of European standard EN 13674 Part 1 for R400HT grade), to investigate the development and controllability of process-induced residual stress in the cladding, heat-affected zone (HAZ) and substrate and to analyse their correlation with hardness profile during two different laser cladding directions (across and along the track). Residual stresses were analysed by neutron diffraction at OPAL reactor, ANSTO. Neutron diffraction was carried out on the samples in longitudinal (parallel to the rail), transverse (perpendicular to the rail) and normal (through thickness) directions with high spatial resolution through the thickness. Due to the thick rail and thin cladding, 4 mm thick reference samples were prepared from every specimen by Electric Discharge Machining (EDM). Metallography across the laser claded sample revealed four distinct zones: The clad zone, the dilution zone, HAZ and the substrate. Compressive residual stresses were found in the clad zone and tensile residual stress in the dilution zone and HAZ. Laser cladding in longitudinally cladding induced higher tensile stress in the HAZ, whereas transversely cladding rail showed lower tensile behavior.

Keywords: Laser cladding, residual stress, neutron diffraction, HAZ.

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Authors: Zhenquan Li, Niharika Singh

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Streamribbon is used to visualize the rotation of the fluid flow. The rotation of flow is useful in fluid mechanics, engineering and geophysics. This paper introduces the construction technique of streamribbon using the streamline which is generated based on the law of mass conservation. The accuracy of constructed streamribbons is shown through two examples.

Keywords: Mass conservation, streamline, streamtube, streamribbon.

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Authors: SuGil Choi, SeungWan Han, Chi-Yoon Jeong, TaekYong Nam

Abstract:

This paper proposes a specialized Web robot to automatically collect objectionable Web contents for use in an objectionable Web content classification system, which creates the URL database of objectionable Web contents. It aims at shortening the update period of the DB, increasing the number of URLs in the DB, and enhancing the accuracy of the information in the DB.

Keywords: Web robot, objectionable Web content classification, URL database, URL rating

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Authors: B. Golchin, N. Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: A. Javed, K. Djidjeli, J. T. Xing, S. J. Cox

Abstract:

A method for simulating flow around the solid bodies has been presented using hybrid meshfree and mesh-based schemes. The presented scheme optimizes the computational efficiency by combining the advantages of both meshfree and mesh-based methods. In this approach, a cloud of meshfree nodes has been used in the domain around the solid body. These meshfree nodes have the ability to efficiently adapt to complex geometrical shapes. In the rest of the domain, conventional Cartesian grid has been used beyond the meshfree cloud. Complex geometrical shapes can therefore be dealt efficiently by using meshfree nodal cloud and computational efficiency is maintained through the use of conventional mesh-based scheme on Cartesian grid in the larger part of the domain. Spatial discretization of meshfree nodes has been achieved through local radial basis functions in finite difference mode (RBF-FD). Conventional finite difference scheme has been used in the Cartesian ‘meshed’ domain. Accuracy tests of the hybrid scheme have been conducted to establish the order of accuracy. Numerical tests have been performed by simulating two dimensional steady and unsteady incompressible flows around cylindrical object. Steady flow cases have been run at Reynolds numbers of 10, 20 and 40 and unsteady flow problems have been studied at Reynolds numbers of 100 and 200. Flow Parameters including lift, drag, vortex shedding, and vorticity contours are calculated. Numerical results have been found to be in good agreement with computational and experimental results available in the literature.

Keywords: CFD, Meshfree particle methods, Hybrid grid, Incompressible Navier Strokes equations, RBF-FD.

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Authors: Omid A. Zargar

Abstract:

In this project three type of tools, straight cylindrical, taper cylindrical and triangular tool all made of High speed steel (Wc-Co) used for the friction stir welding (FSW) aluminum alloy H20–H20 and the mechanical properties of the welded joint tested by tensile test and vicker hardness test. Besides, mentioned mechanical properties compared with each other to make conclusion. The result helped design of welding parameter optimization for different types of friction stir process like rotational speed, depth of welding, travel speed, type of material, type of joint, work piece dimension, joint dimension, tool material and tool geometry. Previous investigations in different types of materials work pieces; joint type, machining parameter and preheating temperature take placed. In this investigation 3 mentioned tool types that are popular in FSW tested and the results completed other aspects of the process. Hope this paper can open a new horizon in experimental investigation of mechanical properties for friction stir welded joint with other different type of tools like oval shape probe, paddle shape probe, three flat sided probe, and three sided re-entrant probe and other materials and alloys like titanium or steel in near future.

Keywords: Friction stir welding (FSW), tool, CNC milling machine, aluminum alloy H20, Vickers hardness test, tensile test, straight cylindrical tool, taper cylindrical tool, triangular tool.

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Authors: Bi-Huei Tsai

Abstract:

Prior research has not effectively investigated how the profitability of Chinese branches affect FDIs in China [1, 2], so this study for the first time incorporates realistic earnings information to systematically investigate effects of innovation, imitation, and profit factors of FDI diffusions from Taiwan to China. Our nonlinear least square (NLS) model, which incorporates earnings factors, forms a nonlinear ordinary differential equation (ODE) in numerical simulation programs. The model parameters are obtained through a genetic algorithms (GA) technique and then optimized with the collected data for the best accuracy. Particularly, Taiwanese regulatory FDI restrictions are also considered in our modified model to meet the realistic conditions. To validate the model-s effectiveness, this investigation compares the prediction accuracy of modified model with the conventional diffusion model, which does not take account of the profitability factors. The results clearly demonstrate the internal influence to be positive, as early FDI adopters- consistent praises of FDI attract potential firms to make the same move. The former erects a behavior model for the latter to imitate their foreign investment decision. Particularly, the results of modified diffusion models show that the earnings from Chinese branches are positively related to the internal influence. In general, the imitating tendency of potential consumers is substantially hindered by the losses in the Chinese branches, and these firms would invest less into China. The FDI inflow extension depends on earnings of Chinese branches, and companies will adjust their FDI strategies based on the returns. Since this research has proved that earning is an influential factor on FDI dynamics, our revised model explicitly performs superior in prediction ability than conventional diffusion model.

Keywords: diffusion model, genetic algorithms, nonlinear leastsquares (NLS) model, prediction error.

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Authors: Y. Z. Wu, Z. Dong, S. K. You

Abstract:

Global approximation using metamodel for complex mathematical function or computer model over a large variable domain is often needed in sensibility analysis, computer simulation, optimal control, and global design optimization of complex, multiphysics systems. To overcome the limitations of the existing response surface (RS), surrogate or metamodel modeling methods for complex models over large variable domain, a new adaptive and regressive RS modeling method using quadratic functions and local area model improvement schemes is introduced. The method applies an iterative and Latin hypercube sampling based RS update process, divides the entire domain of design variables into multiple cells, identifies rougher cells with large modeling error, and further divides these cells along the roughest dimension direction. A small number of additional sampling points from the original, expensive model are added over the small and isolated rough cells to improve the RS model locally until the model accuracy criteria are satisfied. The method then combines local RS cells to regenerate the global RS model with satisfactory accuracy. An effective RS cells sorting algorithm is also introduced to improve the efficiency of model evaluation. Benchmark tests are presented and use of the new metamodeling method to replace complex hybrid electrical vehicle powertrain performance model in vehicle design optimization and optimal control are discussed.

Keywords: Global approximation, polynomial response surface, domain decomposition, domain combination, multiphysics modeling, hybrid powertrain optimization

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Authors: M. Seguini, D. Nedjar

Abstract:

An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: Finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability.

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Authors: Hidetoshi Nonaka, Masahito Kurihara

Abstract:

In this paper, an authentication system using keystroke dynamics is presented. We introduced pressure sensing for the improvement of the accuracy of measurement and durability against intrusion using key-logger, and so on, however additional instrument is needed. As the result, it has been found that the pressure sensing is also effective for estimation of real moment of keystroke.

Keywords: Biometric authentication, Keystroke dynamics, Pressure sensing, Time-frequency analysis.

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Authors: Carol Anne Hargreaves

Abstract:

A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: Machine learning, stock market trading, logistic principal component analysis, automated stock investment system.

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Authors: H. D. Lim, Jie Wu, T. H. New, Shengxian Shi

Abstract:

This paper outlines the development of an experimental technique in quantifying supersonic jet flows, in an attempt to avoid seeding particle problems frequently associated with particle-image velocimetry (PIV) techniques at high Mach numbers. Based on optical flow algorithms, the idea behind the technique involves using high speed cameras to capture Schlieren images of the supersonic jet shear layers, before they are subjected to an adapted optical flow algorithm based on the Horn-Schnuck method to determine the associated flow fields. The proposed method is capable of offering full-field unsteady flow information with potentially higher accuracy and resolution than existing point-measurements or PIV techniques. Preliminary study via numerical simulations of a circular de Laval jet nozzle successfully reveals flow and shock structures typically associated with supersonic jet flows, which serve as useful data for subsequent validation of the optical flow based experimental results. For experimental technique, a Z-type Schlieren setup is proposed with supersonic jet operated in cold mode, stagnation pressure of 4 bar and exit Mach of 1.5. High-speed singleframe or double-frame cameras are used to capture successive Schlieren images. As implementation of optical flow technique to supersonic flows remains rare, the current focus revolves around methodology validation through synthetic images. The results of validation test offers valuable insight into how the optical flow algorithm can be further improved to improve robustness and accuracy. Despite these challenges however, this supersonic flow measurement technique may potentially offer a simpler way to identify and quantify the fine spatial structures within the shock shear layer.

Keywords: Schlieren, optical flow, supersonic jets, shock shear layer.

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

Abstract:

Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.

Keywords: Anomaly detection, dimensionality reduction, frequencies selection, modal analysis, neural network, structural health monitoring, vibration measurement.

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Authors: Ashkan Ebadi, Adam Krzyzak

Abstract:

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite the wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This paper proposes a highly accurate hotel recommender system, implemented in various layers. Using multi-aspect rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing and topic modelling techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system.

Keywords: Tourism, hotel recommender system, hybrid, implicit features.

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

Abstract:

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: Affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, student engagement.

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Authors: M.Suganthi, M.Madheswaran

Abstract:

This paper presents a novel approach to finding a priori interesting regions in mammograms. In order to delineate those regions of interest (ROI-s) in mammograms, which appear to be prominent, a topographic representation called the iso-level contour map consisting of iso-level contours at multiple intensity levels and region segmentation based-thresholding have been proposed. The simulation results indicate that the computed boundary gives the detection rate of 99.5% accuracy.

Keywords: Breast Cancer, Mammogram, and Segmentation.

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Authors: Umesh Ghanekar

Abstract:

As the performance of the filtering system depends upon the accuracy of the noise detection scheme, in this paper, we present a new scheme for impulse noise detection based on two levels of decision. In this scheme in the first stage we coarsely identify the corrupted pixels and in the second stage we finally decide whether the pixel under consideration is really corrupt or not. The efficacy of the proposed filter has been confirmed by extensive simulations.

Keywords: Impulse detection, noise removal, image filtering.

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Authors: Amirhossein Lotfi, Huaizhong Li, Dzung Viet Dao

Abstract:

Interest in natural fiber-reinforced composites (NFRC) is progressively growing both in terms of academia research and industrial applications thanks to their abundant advantages such as low cost, biodegradability, eco-friendly nature and relatively good mechanical properties. However, their widespread use is still presumed as challenging because of the specificity of their non-homogeneous structure, limited knowledge on their machinability characteristics and parameter settings, to avoid defects associated with the machining process. The present work is aimed to investigate the effect of the cutting tool geometry and material on the drilling-induced delamination, thrust force and hole quality produced when drilling a fully biodegradable flax/poly (lactic acid) composite laminate. Three drills with different geometries and material were used at different drilling conditions to evaluate the machinability of the fabricated composites. The experimental results indicated that the choice of cutting tool, in terms of material and geometry, has a noticeable influence on the cutting thrust force and subsequently drilling-induced damages. The lower value of thrust force and better hole quality was observed using high-speed steel (HSS) drill, whereas Carbide drill (with point angle of 130^o) resulted in the highest value of thrust force. Carbide drill presented higher wear resistance and stability in variation of thrust force with a number of holes drilled, while HSS drill showed the lower value of thrust force during the drilling process. Finally, within the selected cutting range, the delamination damage increased noticeably with feed rate and moderately with spindle speed.

Keywords: Natural fiber-reinforced composites, machinability, thrust force, delamination.

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Authors: José Alberto García Fernández, Zhimin Du, Xinqiao Jin

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Nowadays, data center industry faces strong challenges for increasing the speed and data processing capacities while at the same time is trying to keep their devices a suitable working temperature without penalizing that capacity. Consequently, the cooling systems of this kind of facilities use a large amount of energy to dissipate the heat generated inside the servers, and developing new cooling techniques or perfecting those already existing would be a great advance in this type of industry. The installation of a temperature sensor matrix distributed in the structure of each server would provide the necessary information for collecting the required data for obtaining a temperature profile instantly inside them. However, the number of temperature probes required to obtain the temperature profiles with sufficient accuracy is very high and expensive. Therefore, other less intrusive techniques are employed where each point that characterizes the server temperature profile is obtained by solving differential equations through simulation methods, simplifying data collection techniques but increasing the time to obtain results. In order to reduce these calculation times, complicated and slow computational fluid dynamics simulations are replaced by simpler and faster finite element method simulations which solve the Burgers‘ equations by backward, forward and central discretization techniques after simplifying the energy and enthalpy conservation differential equations. The discretization methods employed for solving the first and second order derivatives of the obtained Burgers‘ equation after these simplifications are the key for obtaining results with greater or lesser accuracy regardless of the characteristic truncation error.

Keywords: Burgers’ equations, CFD simulation, data center, discretization methods, FEM simulation, temperature profile.

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Authors: Matthias Feiner, Francisco Javier Fernández García, Michael Arneman, Martin Kipfmüller

Abstract:

To ensure reliability in miniaturized devices or processes with increased heat fluxes, very efficient cooling methods have to be employed in order to cope with small available cooling surfaces. To address this problem, a certain type of evaporator/heat exchanger was developed: It is called a swirl evaporator due to its flow characteristic. The swirl evaporator consists of a concentrically eroded screw geometry in which a capillary tube is guided, which is inserted into a pocket hole in components with high heat load. The liquid refrigerant R32 is sprayed through the capillary tube to the end face of the blind hole and is sucked off against the injection direction in the screw geometry. Its inner diameter is between one and three millimeters. The refrigerant is sprayed into the pocket hole via a small tube aligned in the center of the bore hole and is sucked off on the front side of the hole against the direction of injection. The refrigerant is sucked off in a helical geometry (twisted flow) so that it is accelerated against the hot wall (centrifugal acceleration). This results in an increase in the critical heat flux of up to 40%. In this way, more heat can be dissipated on the same surface/available installation space. This enables a wide range of technical applications. To optimize the design for the needs in various fields of industry, like the internal tool cooling when machining nickel base alloys like Inconel 718, a correlation-based model of the swirl-evaporator was developed. The model is separated into 3 subgroups with overall 5 regimes. The pressure drop and heat transfer are calculated separately. An approach to determine the locality of phase change in the capillary and the swirl was implemented. A test stand has been developed to verify the simulation.

Keywords: Helically-shaped, oil-free, R32, swirl-evaporator, twist flow.

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Authors: Vadim Vagin, Marina Fomina, Oleg Morosin

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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.

Keywords: Argumentation, justification degrees, inductive concept formation, noise, generalization.

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Authors: Syahril Ridzuan Ab Rahim, Ibrahim Ahmad, Mohd Azizi Chik, Ahmad Zafir Md. Rejab, and U. Hashim

Abstract:

This research presents the development of simulation modeling for WIP management in semiconductor fabrication. Manufacturing simulation modeling is needed for productivity optimization analysis due to the complex process flows involved more than 35 percent re-entrance processing steps more than 15 times at same equipment. Furthermore, semiconductor fabrication required to produce high product mixed with total processing steps varies from 300 to 800 steps and cycle time between 30 to 70 days. Besides the complexity, expansive wafer cost that potentially impact the company profits margin once miss due date is another motivation to explore options to experiment any analysis using simulation modeling. In this paper, the simulation model is developed using existing commercial software platform AutoSched AP, with customized integration with Manufacturing Execution Systems (MES) and Advanced Productivity Family (APF) for data collections used to configure the model parameters and data source. Model parameters such as processing steps cycle time, equipment performance, handling time, efficiency of operator are collected through this customization. Once the parameters are validated, few customizations are made to ensure the prior model is executed. The accuracy for the simulation model is validated with the actual output per day for all equipments. The comparison analysis from result of the simulation model compared to actual for achieved 95 percent accuracy for 30 days. This model later was used to perform various what if analysis to understand impacts on cycle time and overall output. By using this simulation model, complex manufacturing environment like semiconductor fabrication (fab) now have alternative source of validation for any new requirements impact analysis.

Keywords: Advanced Productivity Family (APF), Complementary Metal Oxide Semiconductor (CMOS), Manufacturing Execution Systems (MES), Work In Progress (WIP).

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Authors: Rohith K Reddy, David Mayerich, Michael Walsh, P Scott Carney, Rohit Bhargava

Abstract:

Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that provides both chemically and spatially resolved information. The rich chemical content of data may be utilized for computer-aided determinations of structure and pathologic state (cancer diagnosis) in histological tissue sections for prostate cancer. FT-IR spectroscopic imaging of prostate tissue has shown that tissue type (histological) classification can be performed to a high degree of accuracy [1] and cancer diagnosis can be performed with an accuracy of about 80% [2] on a microscopic (≈ 6μm) length scale. In performing these analyses, it has been observed that there is large variability (more than 60%) between spectra from different points on tissue that is expected to consist of the same essential chemical constituents. Spectra at the edges of tissues are characteristically and consistently different from chemically similar tissue in the middle of the same sample. Here, we explain these differences using a rigorous electromagnetic model for light-sample interaction. Spectra from FT-IR spectroscopic imaging of chemically heterogeneous samples are different from bulk spectra of individual chemical constituents of the sample. This is because spectra not only depend on chemistry, but also on the shape of the sample. Using coupled wave analysis, we characterize and quantify the nature of spectral distortions at the edges of tissues. Furthermore, we present a method of performing histological classification of tissue samples. Since the mid-infrared spectrum is typically assumed to be a quantitative measure of chemical composition, classification results can vary widely due to spectral distortions. However, we demonstrate that the selection of localized metrics based on chemical information can make our data robust to the spectral distortions caused by scattering at the tissue boundary.

Keywords: Infrared, Spectroscopy, Imaging, Tissue classification

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Authors: Valeriy Naumov, Olli Martikainen

Abstract:

In this paper we propose new method for simultaneous generating multiple quantiles corresponding to given probability levels from data streams and massive data sets. This method provides a basis for development of single-pass low-storage quantile estimation algorithms, which differ in complexity, storage requirement and accuracy. We demonstrate that such algorithms may perform well even for heavy-tailed data.

Keywords: Quantile estimation, data stream, heavy-taileddistribution, tail index.

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Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

Abstract:

Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: Carbon stock, forest inventory, LiDAR, tree count.

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