An artificial neural network approach on parametric optimization of laser micro-machining of die-steel

In the present research, laser micro machining (LMM) of tungsten-molybdenum general purpose high speed steel (Rex M2) has been studied. Selection of optimum machining parameter combinations for obtaining higher depth of groove and smaller height of recast layer is a challenging task in LMM due to the presence of a large number of process variables. There is no perfect combination of parameters which can simultaneously result in both the highest depth of groove and lowest height of recast layer. This paper presents an attempt to develop a strategy for predicting the optimum machining parameter setting for the generation of the maximum depth of groove with minimum height of recast layer. A feed forward back-propagation neural network has been developed to model the machining process. The model, after proper training, is capable of predicting the response parameters as a function of four different control parameters. Experimental results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements. The developed model has been found to be quite unique, powerful and flexible.     

Retention of Escherichia coli in municipal sewage by means of planted soil filters in two-stage pilot plant systems.

Different types of constructed wetlands in a pilot-plant system were fed with identical municipal waste water to compare the sanitisation process in two-stage systems. With combinations of a vertical and a horizontal flow filter an E. coli reduction of 5 log10 with an E. coli influent concentration of 10(7) MPN/100 ml was achieved. Using different filter materials in each stage the total performance of the two-stage system was independent from the sequence of these materials. However, using coarser filter material in the first stage makes the filter less prone to clogging and is thus the preferential option with regard to operational reliability.     

Preparation and Characterization of Novel Polyimides from 2,6-bis[1-(p-dimethylaminophenylimino)ethyl] pyridine for the Selective Extraction of some Metals

Summary A group of new polyimides has been prepared by solution condensation of 2,6-bis[1-(p-dimethylaminophenylimino)ethyl] pyridine containing pendant NMe₂ units and various dianhyrides in N-methylpyrrolidone (NMP). The tridentate (N-N′-N) pydim ligands were prepared by Schiff-base condensation of 2,6-diacetylpyridine and 4-dimethylaminoaniline in the presence of formic acid as catalyst. These polymers were investigated for their extraction capabilities for Zn(II), Mn(II), Cu(II), Cd (II) and Ni(II) and at different pH. Under different conditions enhanced selectivity was observed. Up to 98% quantitative recoveries were observed for all metals.     

Decision Making Effectiveness in Wildfire Incident Management Teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task‐disruptive developments. These processes appear to be complex multi‐person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.     

A One-Dimensional Nonlocal Damage-Plasticity Model for Ductile Materials

This paper presents a new 1-D non-local damage-plasticity deformation model for ductile materials. It uses the thermodynamic framework described in Houlsby and Puzrin (2000) and holds, nevertheless, some similarities with Lemaitre’s (1971) approach. A 1D finite element (FE) model of a bar fixed at one end and loaded in tension at the other end is introduced. This simple model demonstrates how the approach can be implemented within the finite element framework, and that it is capable of capturing both the pre-peak hardening and post-peak softening (generally responsible for models instability) due to damage-induced stiffness and strength reduction characteristic of ductile materials. It is also shown that the approach has further advantages of achieving some degree of mesh independence, and of being able to capture deformation size effects. Finally, it is illustrated how the model permits the calculation of essential work of rupture (EWR), i.e. the specific energy per unit cross-sectional area that is needed to cause tensile failure of a specimen.     

Optimized hydrogen positions for aluminium and iron containing hydroxide minerals

The structures of a number of hydroxide and oxyhydroxide minerals have previously been reported without the hydrogen positions explicitly defined. Here we use two atomic scale computer simulation techniques, one based on classical ionic potentials, the other on density functional theory (DFT), to predict these positions. The aim is not only to provide data that can be used as the basis for future experimental structure optimizations but also model parameters that can be used to predict complex hydroxide structures. The efficacy of the approach is demonstrated through the comparison of predicted and experimental data for minerals whose hydrogen positions are known.     

Modeling of precipitation hardening in Mg-based alloys

This work deals with the development of Mg-based alloys with enhanced properties at elevated temperatures. This is achieved by precipitation of binary phases such as MgZn₂ and Mg₂Sn during the aging of these alloys. The aim of the present work is to develop and calibrate a model for precipitation hardening in Mg-based alloys, as different types of precipitates form simultaneously. The modified Langer-Schwartz approach, while taking into account nucleation, growth and coarsening of the new phase precipitations, was used for the analysis of precipitates’ evolution and precipitation hardening during aging of Mg-based alloys. Two strengthening mechanisms associated with particle-dislocation interaction (shearing and bypassing) were considered to be operating simultaneously due to particle size-distribution. Parameters of the model, R _N i and k _{σ i} , were found by fitting of calculated densities and average sizes of precipitates with ones estimated from experiments. The effective diffusion coefficients of phase formation processes, which determine the strengthening kinetics, were estimated from the hardness maximum positions on the aging curves.     

Acoustic Properties of Amorphous Solids at Very Low Temperatures: The Quest for Interacting Tunneling States

We discuss the strain dependence of the acoustic properties of amorphous metals in both normal and superconducting states, in the temperature range 0.1 mK<T<1 K. A crossover is found when the strain energy is of the order of the effective interaction energy between tunneling systems at the corresponding temperature. Our results provide clear evidence for the interaction between tunneling systems, whose energy is in quantitative agreement with theoretical expectations, and reveal that without the knowledge of the corresponding strain dependences, the measured temperature dependences below ～50 mK of the acoustic properties of disordered solids are rather meaningless.