Effect of Carbon Content on Variant Pairing in Bainitic Low Alloy Steel

Metallurgical and Materials Transactions A - Variant pairing in bainite was evaluated in four different commercial low alloy steels with medium to high carbon content. The steels investigated were...     

Development of Cr and Al Pack Cementation Coatings on Co-Based γ/γ′ Superalloys

Metallurgical and Materials Transactions A - Co-based superalloys have been developed as candidate materials to replace Ni-based superalloys in hot sections of turbine engines, however, their...     

The action of various lime soap dispersants

The course of the lime soap-dispersing action of various surfactants has been examined by use of a titrimetric method. Two types of dispersants can be distinguished. With the addition of a surfactant of the first type, the concentration of the dispersed fatty acid increases gradually. If a surfactant of the second type is used, a definite increase of the fatty acid concentration first occurs after the addition of considerable amounts of the dispersant. Ethylene oxide adducts and sodium triethylene glycol monolauryl ether sulfate behave like dispersants of the first type whereas sodium dodecylbenzenesulphonate and sodium lauryl sulfate belong to those of the second type. The dispersing action of both types of surfactants is discussed.     

Bücherschau

Ohne Zusammenfassung VDI     

Numerical characterization and experimental verification of an in-plane MEMS-actuator with thin-film aluminum heater

In this paper, a novel concept of a thermo-mechanical MEMS actuator using aluminum thin-film heaters on a thermal oxide for electrical insulation is presented. The actuator is part of an universal tensile testing platform for thermo-mechanical material characterization of one dimensional materials on a micro- and nano-scopic scale under different environmental conditions, as varying temperatures, pressure, moisture or even vacuum and is realised in BDRIE technology. It is shown, that the actuator concept fulfills the requirements for the use in a tensile loading stage along with heterogeneously integrated nanofunctional elements, following a specimen centered approach in line with bottom-up self-assembly processes. Simulation and experiment agree very well in the thermal and mechanical domain and allow subsequent optimisation of the actuator performance.     

Wafer bonding using Cu–Sn intermetallic bonding layers

Wafer-level Cu–Sn intermetallic bonding is an interesting process for advanced applications in the area of MEMS and 3D interconnects. The existence of two intermetallic phases for Cu–Sn system makes the wafer bonding process challenging. The impact of process parameters on final bonding layer quality have been investigated for transient liquid phase wafer-level bonding based on the Cu–Sn system. Subjects of this investigation were bonding temperature profile, bonding time and contact pressure as well as the choice of metal deposition method and the ratio of deposited metal layer thicknesses. Typical failure modes in intermetallic compound growth for the mentioned process and design parameters have been identified and were subjected to qualitative and quantitative analysis. The possibilities to avoid abovementioned failures are indicated based on experimental results.     

Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms

Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.     

Surface and subsurface products of the interaction of O2 with Ag under catalytic conditions

Polycrystalline Ag treated with O₂ at pressures up to 1 bar and at temperatures up to 920 K was examined by TDS. It was found that the peak commonly observed at 595±25 K and assigned to adsorbed atomic oxygen (O_a) is shifted to higher desorption temperatures with rising dosing temperatures. Since saturation of the uptake was not possible either, this peak was identified as dissolved atomic oxygen (O_d). The existence of a maximum for the O₂ uptake after exposure at 820 K was found. Hence, under conditions of formaldehyde synthesis ( 920 K) the ability of the catalyst to dissolve oxygen is near its maximum.     

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.