Precorroded steel A-106 B specimens were prepared at different surface roughness. These specimens were immersed in corrosive ferric chloride solution in different concentrations (1.5, 3.0, 4.5, 6.0% wt.) at specified durations to initiate primarily the pitting corrosion. The corrosion pits distribution depend on the corrosive concentration, degree of surface roughness, and immersion duration.The pits were characterized using metallurgical microscope. Also, The pitting characteristics aimed to be predicted by "Artificial Neural Networks" (ANNs). The results obtained of pit quantification by ANNs predictions are shown to be agreed well against experimental values. i.e. R2=0.9839
This work aims to produce an experimental and theoretical analysis of thermal insulator specifications for buildings with sustainable requirements. In the experimental work, three categories of thermal insulators were prepared from composite materials, and each category had ten models. These composites included the addition of two types of waste (sawdust and tyre waste) as fillers for two types of matrices (liquid polyurethane and polyurethane foam) to obtain composite materials for thermal insulation samples. The prepared samples were subjected to tests to show their thermal properties, such as thermal conductivity and specific heat capacity as well as undergoing a hardness test. The theoretical analysis included the discovery of empirical equations for thermal properties as functions of two variables (temperature and mass ratio) and hardness as a function of one variable (mass ratio). A genetic algorithm optimisation technique was used to find the optimum mass ratio of the composite that produced the required insulation specifications. The results showed that thermal conductivity decreased when the sawdust mass ratio and the rubber waste mass ratio increased but remained under the thermal insulation range. Furthermore, the prepared insulator samples showed an improvement in thermal storage and the hardness of tyre waste (liquid polyurethane composites) and sawdust (polyurethane foam composites). Finally, optimum results were obtained using the optimisation technique.
Aim: To characterize and classify stroke lesions and normal brain tissue in computed tomography (CT) images using statistical texture descriptors. Patients and methods: Two experienced radiologists blinded to each other inspected CT images of 164 stroke patients to identify and categorize stroke lesions into ischaemic and haemorrhagic subtypes. Four regions of interest (ROIs) in each CT slice that demonstrated the lesion; two each representing the lesion and normal tissue were selected. Statistical texture descriptors namely, co-occurrence matrix, run-length matrix, absolute gradient and histogram were calculated for them. Raw data analysis was performed to identify the parameters that best discriminate between normal brain tissue and stroke lesions. Artificial neural network (ANN) was used to classify the ROIs into normal tissue, ischaemic and haemorrhagic lesions using the radiologists’ identification and categorization as the gold standard, and further analyzed using the receiver operating characteristic curve. Results: Three parameters in each texture class discriminated between normal tissue, ischaemic and haemorrhagic stroke lesions. The discriminating co-occurrence matrix parameters were sum average parameters namely S1-1 SumAverg, S1-0 SumAverg and S0-1 SumAverg. For the run-length matrix, short run emphasis in horizontal, 1350 and 450 directions were the discriminating features. The discriminating absolute gradient parameters were gradient non-zeros, gradient variance and gradient mean. For the histogram class, the mean, 90th and 99th percentiles were the discriminating parameters. The ANN achieved a sensitivity of 0.637, specificity 0.753, false positive rate (FPR) 0.247, and false negative rate (FNR) 0.363 with co-occurrence matrix. With run-length matrix the sensitivity was 0.544, specificity 0.607, FPR 0.393, and FNR 0.456 while with absolute gradient the sensitivity was 0.546, specificity 0.586, FPR 0.414, FNR 0.454. With histogram, the sensitivity was 0.947, specificity 0.962, FPR 0.038, and FNR 0.053. Conclusion: The histogram texture features showed the highest sensitivity and specificity in the classification of brain tissue and stroke lesions using the artificial neural network.
The current study aims to find the optimum cutting parameters in turning process without using cutting fluids (dry cutting condition) towards sustainable manufacturing. Where the power consumption and environmental pollution increase due to increase of the machining operations in manufacturing field, so to save energy and environment and reduce cost it is important to adopt sustainability in machining processes.The experimental work in this study involves the preparation to a number of experiments on AISI 1045 carbon steel to collect the necessary data for implementing optimization process. The experiments were conducted by changing levels of cutting parameters (spindle speed, feed rate and cutting depth) in CNC turning machine. Surface roughness of the workpiece has been depended as a quality indicator. In addition, the temperature of cutting tool has been recorded during machining the work pieces in order to control the temperature of cutting process.Theoretically, empirical equations for temperature of cutting tool and surface roughness of the work piece have been discovered. By using Genetic Algorithm technique these equations have been used to find the optimum of cutting parameters spindle speed, feed rate and depth of cut.The optimum values that obtained by using Genetic Algorithm which achieve sustainable cutting were spindle speed 588.96 rpm, depth of cut 0.50 mm and feed rate 64.55 mm/min in order to have the optimum of surface roughness in low cutting temperature.
The present work includes a study on the effect of loading rubber waste into cement mortar on the thermal and mechanical properties of a thermal insulator.The experimental work of the study included the preparation of ten models of 35 mm diameter and 5 mm thickness. Portland cement and natural sand were used as a matrix and rubber waste (extracted from the consumed tires) as a filler was added in weight percentages ( 5% ,10% ,15% ,20% ,25% ,30% ,35% ,40%,45% and 50%). Water was also used as a binder.Also, the experimental work included conducting a thermal conductivity test using Lee’s Disk method, and a hardness test using the Shore scale. The theoretical side included extraction of empirical equations, depending on the experimental results. The thermal conductivity equation was for two variables, temperature and mass fraction. While the hardness equation was for one variable, mass fraction. Theoretically determined heat capacity was extracted using the equations of the composites. Based on the empirical equations of thermal conductivity and hardness and using the technique of multi-objectives genetic algorithm, the optimum values of temperature and mass fraction were extracted, which achieve the best thermal insulation of the mortar.The results showed a significant decrease in thermal conductivity. The reduction in thermal conductivity was (90.3%) at 5% and reduced to (95.73%) at 50%. The specific heat capacity was increasing as the percentage of rubber waste increase. The results also indicated a decrease in hardness. The optimal value of thermal insulation was (0.02658 W/m2.ºC ) as a thermal conductivity and (58.07 N/m2) as a hardness, at temperature (50°C) and mass fraction (27.764%) of rubber waste.Index Terms— rubber wastes ,empirical data , genetic algorithm.
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