Improved contact load-bearing capacity of ultra-fine grained titanium : Multilayering and grading

The contact load-bearing response and surface damage resistance of multilayered hierarchical structured (MHSed) titanium were determined and compared to monolithic nanostructured titanium. The MHS structure was formed by combining cryorolling with a subsequent Surface Mechanical Attrition Treatment (SMAT) producing a surface structure consisted of an outer amorphous layer containing nanocrystals, an inner nanostructured layer and finally an ultra-fine grained core. The combination of a hard outer layer, a gradual transition layer and a compliant core results in reduced indentation depth, but a deeper and more diffuse sub-surface plastic deformation zone, compared to the monolithic nanostructured Ti. The redistribution of surface loading between the successive layers in the MHS Ti resulted in the suppression of cracking, whereas the monolithic nanograined (NG) Ti exhibited sub-surface cracks at the boundary of the plastic strain field. Finite element models with discrete layers and mechanically graded layersrepresenting the MHS system confirmed the absence of cracking and revealed a 38% decrease in shear stress in the sub-surface plastic strain field, compared to the monolithic NG Ti. Further, the mechanical gradation achieves a more gradual stress distribution which mitigates the interface failure and increases the interfacial toughness, thus providing strong resistance to loading damage. © 2014 Elsevier Ltd.

Active surface shaping for artificial skins

Artificial skins exhibit different mechanical properties in compare to natural skins. This drawback makes physical interaction with artificial skins to be different from natural skin. Increasing the performance of the artificial skins for robotic hands and medical applications is addressed in the present paper. The idea is to add active controls within artificial skins in order to improve their dynamic or static behaviors. This directly results into more interactivity of the artificial skins. To achieve this goal, a piece-wise linear anisotropic model for artificial skins is derived. Then a model of matrix of capacitive MEMS actuators for the control purpose is coupled with the model of artificial skin. Next an active surface shaping control is applied through the control of the capacitive MEMS actuators which shapes the skin with zero error and in a desired time. A simulation study is presented to validate the idea of using MEMS actuator for active artificial skins. In the simulation, we actively control 128 capacitive micro actuators for an artificial fingertip. The fingertip provides the required shape in a required time which means the dynamics of the skin is improved.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

Effect of membrane stress on surface roughness changes in sheet forming

Friction plays an important role in sheet metal forming (SMF) and the roughness of the surface of the sheet is a major factor that influences friction. In finite element method (FEM) models of metal forming, the roughness has usually been assumed to be constant; even though it is commonly observed that sheet drawn under tension over a tool radius results in the surface becoming shiny, indicating a major change in surface morphology. An elastic–plastic FEM model for micro-contact between a flat surface and a single roughness peak has been developed. The model was used to investigate the effect of the membrane stress in the sheet on the deformation of an artificial roughness peak. From the simulation results, the change in asperity, or deformation of the local peak, for a given nominal tool contact stress is significantly influenced by the local substrate stress. The height of the asperity decreases with increasing substrate stress and the local pressure is much higher than the nominal pressure. In addition, the local contact stress decreases with an increase in the substrate stress levels.

Larval settlement of the tropical abalone, Haliotis asinina Linnaeus, using natural and artificial chemical inducers

Many kinds of chemical and biological materials have been used as inducers of settlement of abalone larvae, as well as other species of marine gastropods, with responses being highly variable, even to the same chemical cue. The present study tested chemical inducers, γ-aminobutyric acid (GABA), δ-aminovaleric acid (5-AVA) and l-glutamic acid (GA) and the effects they have on larval settlement of Haliotis asinina. Additionally, a relatively inexpensive commercial substance, monosodium glutamate (MSG), was trialed. The datum provided shows all chemicals to be active inducers of settlement in this study, in order of effectiveness of 5-AVA, GABA, MSG to GA. Induction as adjudged from larval numbers settled was best at 6 h 62%, with 10⁻¹ mM 5-AVA. At 24 h, induction was the highest at 78% when exposed to 10⁻² mM 5-AVA. Larvae that were allowed to settle up to 72 h showed the highest numbers of settled larvae, and declined back to 60% when exposed to 10⁻² 5-AVA and 10⁻¹ mM GABA respectively. Monosodium glutamate, although third in settlement standings would bypass the other chemicals, with regard to cost versus yield. The assessment of settlement surface, rough or smooth proved to be irrelevant, which had no significant impact on larval settlement.

Artificial neural network analysis of twin tunnelling-induced ground settlements

In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.

Artificial water points facilitate the spread of an invasive vertebrate in arid Australia

Summary: The spread of invasive species after their initial introduction is often facilitated by human actions. In some cases, invaders only become established in habitats where dominant native species have been displaced as a result of human actions or where humans inadvertently provide essential resources such as food, water or shelter. We investigated if dams that provide water for livestock have facilitated the cane toad's (Rhinella marina) invasion of a hot semi-arid landscape by providing toads with a resource subsidy and hence refuge from extreme heat and aridity. To determine the relationship between the presence of surface water and habitat occupancy by toads, we surveyed natural and artificial water features for cane toads during the annual dry season. We used radiotracking and acoustic tags to determine whether movement patterns and shelter use of cane toads were focussed around dams. To determine whether dams provide toads with refuge from extreme heat and aridity, we deployed plaster models with internal thermometers to estimate ambient temperatures and toad desiccation rates in shelter sites. To determine whether dams alleviate the stress experienced by toads, we measured plasma corticosterone levels of toads that sheltered in and away from dams. Toads were present in sites with standing water and absent from waterless sites. Most radiotracked toads sheltered within 1 m of water. Toad movements were focussed around water. Toads tracked with passive acoustic telemetry over a 6-month dry season were highly resident at dams. Plaster models placed in toad shelter sites away from the water lost 27% more mass and experienced higher temperatures than models placed near the water's edge. Toads that sheltered in terrestrial shelters exhibited higher plasma corticosterone levels compared to toads that sheltered near dams. Dams provide toads with refuge habitats where they are less at risk from overheating and dehydration. Synthesis and applications. Artificial water points can facilitate biological invasions in arid regions by providing a resource subsidy for water-dependent invasive species. Our study suggests that there is scope to control populations of water-dependent invasive vertebrates in arid regions by restricting their access to artificial water points.

Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083)

The quality of a machined finish plays a major role in the performance of milling operations, good surface quality can significantly improve fatigue strength, corrosion resistance, or creep behaviour as well as surface friction. In this study, the effect of cutting parameters and cutting fluid pressure on the quality measurement of the surface of the crest for threads milled during high speed milling operations has been scrutinised. Cutting fluid pressure, feed rate and spindle speed were the input parameters whilst minimising surface roughness on the crest of the thread was the target. The experimental study was designed using the Taguchi L32 array. Analysing and modelling the effective parameters were carried out using both a multi-layer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). These were shown to be highly adept for such tasks. In this paper, the analysis of surface roughness at the crest of the thread in high speed thread milling using a high accuracy optical profile-meter is an original contribution to the literature. The experimental results demonstrated that the surface quality in the crest of the thread was improved by increasing cutting speed, feed rate ranging 0.41-0.45 m/min and cutting fluid pressure ranging 2-3.5 bars. These outcomes characterised the ANN as a promising application for surface profile modelling in precision machining.

Microstructural and surface texture analysis due to machining in Super Austenitic Stainless Steel

Inferior surface quality is a significant problem faced by machinist. The purpose of this study is to present a surface texture analysis undertaken as part of machinability assessment of Super Austenitic Stainless Steel alloy-AL6XN. The surface texture analysis includes measuring the surface roughness and investigating the microstructural behaviour of the machined surfaces. Eight milling trials were conducted using combination of cutting parameters under wet machining. An optical profilometer (non-contact), was used to evaluate the surface texture at three positions. The surface texture was represented using the parameter, average surface roughness. Scanning Electron Microscope was utilised to inspect the machined surface microstructure and co relate with the surface roughness results. Results showed that maximum roughness values recorded at the three positions in the longitudinal direction (perpendicular to the machining grooves) were 1.21 μm (trial 1), 1.63 μm (trial 6) and 1.68 μm (trial 7) respectively whereas the roughness values were greatly reduced in the lateral direction. Also, results showed that the feed rate parameter significantly influences the roughness values compared to the other cutting parameters. The microstructure of the machined surfaces was distorted by the existence of cracks, deformed edges and bands and wear deposition due to machining process.

Premature thermal fatigue failure of aluminium injection dies with duplex surface treatment

The premature failure of an aluminium injection die with a duplex surface treatment (plasma nitriding and physical vapor deposition coating) was investigated, in an effort to identify the causes of such premature failure of the component. The manufacturing and the operating conditions were documented. Analytical tools were used, including scanning electron microscopy with energy dispersive X-ray capability, X-ray diffraction, and instrumented microhardness testing. Preliminary observations showed a microstructure of coarse tempered martensite, and a considerably rough surface with porosity and cracks. A detailed analysis of crack initiation sites identified sulfur inclusions in the subsurface, underneath the coating. A further revision of the processing conditions revealed that a sulfur-impregnated grinding stone had been used to polish the die. The chemical composition of such grinding stone matched that of the inclusions found in the subsurface of the failed component. Thus, searched causes of premature failure could be discussed on the lights of the present findings.