Mikro derin çekme işleminde numune ve tribolojik boyut etkisinin sonlu eleman analiziyle incelenmesi

Konferans Bildirisi -- Teorik ve Uygulamalı Mekanik Türk Milli Komitesi, 2013Conference Paper -- Theoretical and Applied Mechanical Turkish National Committee, 2013Bu çalışmada nümune boyut etkisi ve sürtünme katsayısının bir mikro derin çekme parçasının şekillendirilmesine etkisi sonlu eleman analizi yardımıyla incelenmiştir. Boyut etkisini incelemek amacıyla 0.1 , 0.2 ve 1 mm kalınlıklarında 3 farklı sac kullanılmış; kalıp ve zımbaya ait ölçüler de her bir işlemde aynı oranda arttırılarak 3 farklı sonlu eleman modeli oluşturulmuştur. Sac kalınlığının azaltılmasıyla eşdeğer von Mises gerilmesi değerlerinin arttığı tespit edilmiştir. Tribolojik boyut etkisinin belirlenmesi amacıyla yapılan analizlerde ise sürtünme katsayısının arttırılması durumunda eşdeğer gerilmelerde artış gözlenmiş ve 0.1 mm sac kalınlığı için en yüksek eşdeğer gerilme değeri elde edilmiştir.Effects of specimen size and coefficient of friction on micro-deep drawn part have been investigated by means of finite element analysis. To this goal, blanks with three different thicknesses, namely 0.1, 0.2, and 1 mm, have been used. Furthermore, dimensions for die and punch are increased by same factor in FE models. Results showed that, equivalent von Mises stress values are increased when thinner sheet gauges are used. Similarly, increased equivalent von Mises stress values are observed with increasing coefficient friction values, and the highest values are obtained for the smallest sheet thickness

Letter from a Supporter in the Dominican Republic to Geraldine Ferraro

Letter from a supporter in the Dominican Republic to Geraldine Ferraro. Letter includes a Library of Congress translation.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_international/1301/thumbnail.jp

A case of colistin-induced fixed drug eruption

Several medicines, especially antimicrobials, play a rolein the etiology of fixed drug eruption (FDE). The clinicalmanifestation is quite typical for a drug-induced reaction.FDE which developed in an 83-year-old male patientwho has been administered colistin due to Acinetobacterpneumonia is presented here since it is very rarely seen.Therefore colistin should also be considered in the differentialdiagnosis of FDE. J Clin Exp Invest 2013; 4 (3):374-376Key words: Fixed drug eruption, etiology, colisti

Effects of Hybrid Drying on Kinetics, Energy Analysis and Bioactive Properties of Sour Black Mulberry (Morus nigra L.)

Abstract Due to the short harvest season and their sensitivity to storage, the preservation of fresh mulberry fruits is a very important process. Drying is a method used to preserve mulberry fruits in the long term. In this study, response surface methodology (RSM) was applied for the optimization of hybrid drying conditions of two different sour black mulberries. The linear and interaction effects of independent parameters such as temperature (50, 60 and 70°C) and microwave power (100, 200 and 300 W) variables were determined on mulberries. Bioactive properties and energy aspects were monitored as influenced by drying conditions. According to the results increase in microwave power provided a significant decrement in the specific energy consumption (SEC) and the total anthocyanin content (TAC), while increase in the energy efficiency (ηen) and total phenolic content (TPC) for both genotypes. In all cases, statistical values showed that all drying curves of black mulberry were best described by the Logistic model. Multiple response optimization was carried out for studied parameters and it was concluded that maximum antiradical activity (ARA), TPC, TAC, ηen and minimum drying time (DT) and SEC values would be at 300 W-50 ºC (desirability=0.842) and 300 W-66.5 ºC (desirability=0.744), for Morus nigra 1 (MN1) and Morus nigra 2 (MN2), respectively. According to the finding, the greatest TPC, ARA, TAC, DT, SEC and ηen were determined as 20.10 mg GAE/g, 86.00%, 456 mg/kg, 330 min, 18.59 kWh/kg and 9.04% for MN1, and 18.08 mg GAE/g, 83.92%, 835.81 mg/kg, 330 min, 16.16 kWh/kg and 10.40 % for MN2, respectively

MACHINE LEARNING BASED ESTIMATION OF DRYING CHARACTERISTICS OF APPLE SLICES

Machine learning algorithms have been commonly used in food drying processing. These algorithms are also effectively used for nonlinear processes. Estimation of drying characteristics is important for optimizing drying conditions. Estimating the properties such as moisture content, moisture rate and drying rate ensures accurate and high quality drying of the product under air-convective drying conditions. In this study, moisture ratio and drying rates of apple slices were estimated in air-convective drying conditions. Three machine learning algorithms (random forest-RF; artificial neural network-ANN; and k-nearest neighbors-kNN) were performed to estimate moisture ratio and drying rate. In the study, correlation coefficients were found to be above 0.85 in the estimation of humidity and drying rate for all algorithms. The present findings show that machine learning algorithms could be successfully applied for the estimation of drying characteristics.</p

Machine Learning for Varietal Binary Classification of Soybean (Glycine max (L.) Merrill) Seeds Based on Shape and Size Attributes

The most important principal quality attributes of seeds are shape, size, and mass. These parameters play a critical role inthe design of classifer and grading machines. This study was conducted to develop classifcation models for distinguishing the soybean seeds based on shape, size, and mass attributes. The seeds of soybean varieties of Bravo, Ceyhan, Çevik,İlksoy, and Traksoy were classifed in pairs. Four diferent machine learning algorithms (random forest, RF; support vectormachine, SVM; Naïve Bayes, NB; and multilayer perceptron, MLP) were used to evaluate the classifcation performance.In all cases, the soybean seeds of Ceyhan and Traksoy varieties were classifed with the greatest accuracy as 90.00% for theRF classifer and 89.00% for MLP. The variety pairs that followed these varieties with the highest accuracy were Çevik andİlksoy (88.00%, MLP) and Çevik and Traksoy (87.50%, RF). The highest mass (0.19&nbsp;g), volume (155.02 mm3), geometricmean diameter (6.65&nbsp;mm), and projected area (34.80 mm2) values were obtained from Traksoy variety. The Pillai trace andWilks’ lambda results revealed that diferences in physical attributes of the soybean varieties were signifcant (p&lt;0.01). InWilks’ lambda statistics, the unexplained part of the diferences between the groups was found to be 23.0%. Traksoy andÇevik varieties with the highest Mahalanobis distances had similar attributes. Present fndings showed that MLP and RFcould potentially be used for the&nbsp;classifcation of soybean varieties.</p