Rapid determination of the fracture toughness of metallic materials using circumferentially notched bars

Three types of material whose fracture toughness tests were previously performed by using circumferentially notched bars, namely (1) a dual-phase steel with three different morphologies; (2) an Al-Zn-Mg-Cu-wrought alloy; and (3) Al-Si-cast alloys with three different Si contents, were investigated in terms of accuracy and reliability of the testing method. Also, the advantages of using circumferentially notched bars for fracture toughness determination of metallic materials were discussed. With the help of stress concentration factors, which are associated with the bluntness of the notch, correction factors for the fracture toughness calculations are derived. The corrected fracture toughness values are found to be close to the uncorrected ones, implying that the testing procedure is reliable.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Cold Plasma Treatment of Soybean Oil with Hydrogen Gas

High-voltage atmospheric cold plasma (HVACP) treatment generates reactive gas species that induce inter- and intramolecular reactions in soybean oil. The goal of this study is to analyze the effect of HVACP treatment on the chemical structure of soybean oil in a hydrogen gas environment at atmospheric pressure. HVACP was used to treat soybean oil (15 g) for up to 6 hours by triplicate. Plasma-generated reactive gas species interact with the sample, producing three distinct fractions identified as a liquid, gel, and solid. Fatty acid profile, Fourier-transform infrared spectroscopy, proton and carbon nuclear magnetic resonance, size-exclusion chromatography, thermal properties, and peroxide value were used to characterize the chemical structure. Results indicated a lower content of polyunsaturated fatty acids, increased content of saturated fatty acids, and the presence of isomers. An insoluble portion was observed in the solid fraction and increased with treatment time up to 42% in the 6-h treated samples. Plasma species may cause two main reactions: polymerization and hydrogenation.     

Modeling discrete event scalable network systems

Scalability in simulation tools is one of the most important traits to measure performance of software. The reason is that today’s Internet is the main instance of a large-scale and highly complex system. Simulation of Internet-scale network systems has to be supported by any simulation tool. Despite this fact, many network simulators lacks support for building large models. In this work, in order to propose a new approach for scalability issue in network simulation tools, a network simulator is developed based on behavior of honeybees and high performance DEVS, modular and hierarchical system theoretic approach. A biologically-inspired discrete event modeling approach is described for studying networks’ scalability and performance traits. Since natural systems can offer important concepts for modeling network systems, key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the discrete event system specification approach. Large-scale network models are simulated and evaluated to show the benefits of nature-inspired network models.     

The hydro-abrasive erosion wear behavior of duplex-treated surfaces of AISI H13 tool steel

The influence of duplex surface treatments consisting of a DC-pulsed plasma nitriding process and subsequent coatings of CrN and TiAlN deposited by physical vapor deposition(PVD)on AISI H13 tool steel was studied in this article.The treated samples were characterized using metallographic techniques,SEM,EDS,and microhardness methods.Hydro-abrasive erosion wear tests were performed in a specifically designed wear tester in which the samples were rotated in a wear tank containing a mixture of distilled water and ceramic abrasive chips with a fixed rotational speed.The wear rates caused by the abrasive particle impacts were assessed based on accumulated weight loss measurements.The worn surfaces were also characterized using optical microscopy,SEM,and EDS.Microhardness measurements indicated a significant increase in the surface hardness of the duplex-treated samples.The surfaces of the samples with the TiAlN coating were approximately 15 times harder than that of the untreated samples and 3 times that of the plasma nitrided samples.Hydro-abrasive erosion wear results showed that the duplex surface treatments,especially the CrN coating,displayed the highest erosion wear resistance.     

The influence of manganese addition on mechanical properties of commercial Al‐4Mg casting alloys

The aim of this study was to investigate the mechanical properties of Al‐Mg‐Si alloys aged to peak hardness with different dispersoid volume fraction. It was found that the tensile strength increases with dispersoid content, for alloys having similar ductility. The effect of an increasingly triaxial stress state on a fracture strain above mentioned alloys were measured using a series of notched tensile specimens whose notch root radius of curvature was changed. The alloy ductility was found to increase with dispersoid content and root radius and to decrease with increased stress triaxiality. The fracture toughness of these alloys was determined as a function of dispersoid content and notch root radius of curvature. It was observed that the fracture toughness increased as the dispersoid content and the notch root radius increased. scanning electron microscope analysis of the fracture surfaces revealed that fracture mechanism was transgranular fracture with dimples formation. It is argued that optimum mechanical properties in these alloys can be achieved at about 0.5 % Mn content.     

Comparison of OR and AI methods in discrete manufacturing using fuzzy logic

Since 1950s the techniques of Operations Research (OR) and Optimization have been utilized to increase the efficiency of the production systems. With the widespread use of computers, it has even become easier to deal with industrial problems. However the complexity of the problems still reveals the difficulty in providing solutions. The use of artificial intelligence (AI) seems to attract the attention of the researcher to overcome to the difficulties. This has already been realized with several successful applications. In this study, the use of AI and OR techniques is compared using fuzzy logic. The progress of manufacturing systems, characteristics of production processes, system managements and system behavior are taken into account. The study is focussed on only discrete manufacturing.