Hot-wire cutting is one of the important, non-traditional thermomechanical way to cut polymer, usually expanded foam and extruded foam, in low volume manufacturing. The study and analysis of Hot-Wire cutting parameters play an important role to enhance the quality and accuracy of the process and products. The effects on the surface have been investigated by using experimental tests designed according to the Taguchi orthogonal array (OA). In this study, four parameters with five levels for each parameter have been used: [temperature of wire (A) (100, 120, 130, 150, 160) °C], [diameter of wire (B) (0.3,0.4,0.5,0.7,0.8) mm], [velocity of cutting (C) (200, 300,400,500,600) mm/min], [and density of foam (D) (0.01,0.027,0.029,0.032,0.037) g/cm3]. Statistical software (MINITAB17) used to find the optimum conditions, which they are in Material Removal: 100 ˚C, 0.5 mm, 300mm/min, 0.032 g/cm3.
Computers have been used for numerous applications involving the automatic or semiautomatic recognition of patterns in image. Advanced manufacturing system requires automated inspection and test method to increase production and yield best quality of product. Methods are available today is machine vision. Machine vision systems are widely used today in the manufacturing industry for inspection and sorting application. The objective of this paper is to apply machine vision technology for measuring geometric dimension of an automotive part. Vision system usually requires reprogramming or parameterization of software when it has to be configured for a part or product. A web camera used to capture an image of an automotive part that has been ch
... Show MorePhase change materials (PCMs) such as paraffin wax can be used to store or release large amount of energy at certain temperature at which their solid-liquid phase changes occurs. Paraffin wax that used in latent heat thermal energy storage (LHTES) has low thermal conductivity. In this study, the thermal conductivity of paraffin wax has been enhanced by adding different mass concentration (1wt.%, 3wt.%, 5wt.%) of (TiO2) nano-particles with about (10nm) diameter. It is found that the phase change temperature varies with adding (TiO2) nanoparticles in to the paraffin wax. The thermal conductivity of the composites is found to decrease with increasing temperature. The increase in thermal conductivity ha
... Show MoreThe present study investigated the impact of fuel kind on the emitted emissions at the idling period. Three types of available fuels in Iraq were tested. The tests conducted on ordinary gasoline with an octane number of 82, premium gasoline with an octane number of 92, and M20 (consist of 20% methanol and 80% regular gasoline). The 2 liters Mercedes-Benz engine was used in the experiments.
The results showed that engine operation at idle speed emits high levels of CO, CO2, HC, NOx and noise. The produced emission levels depend highly on fuel type. The premium gasoline (ON=92) represents the lower emissions level except for noise at all idling speed. Adding methanol to ordinary gasoline (ON=82) showed high levels of emi
... Show MoreNegotiation is considered as one of the most important kinds of communication in the contemporary organizations, which depend on the important role of managerial information systems in providing necessary and suitable information for success of the negotiation process.
Accordingly, this study aims at measuring the extent of the variables effect of managerial information system in the negotiation process.
To achieve this study, two hypotheses were chosen; the first is the correlation relation and the second is the effect, and statistical means represented by correlation coefficient "Spearman" and (R2) were used.
A Number of conclusions were
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreShaky Baghdad heavy crude oil 22 API is processed by distillation and solvent extraction. The purpose of distillation is to separate the light distillates (light fractions) which represent 35% of heavy crude oil, and to obtain the reduced crude oil. The heavy residue (9 API) is extracted with Iraqi light naphtha to get the deasphaltened oil (DAO), the extraction carried out with temperature range of 20-75 oC, solvent to oil ratio 5-15:1(ml:g) and a mixing time of 15 minutes. In general, results show that API of DAO increased twice the API of reduced crude oil while sulfur and metals content decreased 20% and 50% respectively. Deasphaltened oil produced from various operating conditions blended with the
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Binary logistic regression model used in data classification and it is the strongest most flexible tool in study cases variable response binary when compared to linear regression. In this research, some classic methods were used to estimate parameters binary logistic regression model, included the maximum likelihood method, minimum chi-square method, weighted least squares, with bayes estimation , to choose the best method of estimation by default values to estimate parameters according two different models of general linear regression models ,and different s
... Show MoreAbstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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