Artificial neural network optimisation of shielding gas flow rate in gas metal arc welding subjected to cross drafts when using alternating shielding gases

This study implemented an iterative experimental approach in order to determine the shielding gas flow required to produce high quality welds in the gas metal arc welding (GMAW) process with alternating shielding gases when subjected to varying velocities of cross drafts. Thus determining the transitional zone where the weld quality deteriorates as a function of cross draft velocity. An Artificial Neural Network (ANN) was developed using the experimental data that would predict the weld quality based primarily on shielding gas composition, alternating frequency and flowrate, and cross draft velocity, but also incorporated other important input parameters including voltage and current. A series of weld trials were conducted validate and test the robustness of the model generated. It was found that the alternating shielding gas process does not provide the same level of resistance to the adverse effects of cross drafts as a conventional argon/carbon dioxide mixture. The use of such a prediction tool is of benefit to industry in that it allows the adoption of a more efficient shielding gas flow rate, whilst removing the uncertainty of the resultant weld quality

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Defect-Seeded Atomic Layer Deposition of Metal Oxides on the Basal Plane of 2D Layered Materials

Atomic layer deposition (ALD) on mechanically exfoliated 2D layered materials spontaneously produces network patterns of metal oxide nanoparticles in triangular and linear deposits on the basal surface. The network patterns formed under a range of ALD conditions and were independent of the orientation of the substrate in the ALD reactor. The patterns were produced on MoS2 or HOPG when either tetrakis(dimethylamido)titanium or bis(ethylcyclopentadienyl)manganese were used as precursors, suggesting that the phenomenon is general for 2D materials. Transmission electron microscopy revealed the presence, prior to deposition, of dislocation networks along the basal plane of mechanically exfoliated 2D flakes, indicating that periodical basal plane defects related to disruptions in the van der Waals stacking of layers, such as perfect line dislocations and triangular extended stacking faults networks, introduce a surface reactivity landscape that leads to the emergence of patterned deposition

A Path to Implement Precision Child Health Cardiovascular Medicine.

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine

Thermodynamic parameters of bonds in glassy materials from viscosity-temperature relationships

Doremus's model of viscosity assumes that viscous flow in amorphous materials is mediated by broken bonds (configurons). The resulting equation contains four coefficients, which are directly related to the entropies and enthalpies of formation and motion of the configurons. Thus by fitting this viscosity equation to experimental viscosity data these enthalpy and entropy terms can be obtained. The non-linear nature of the equation obtained means that the fitting process is non-trivial. A genetic algorithm based approach has been developed to fit the equation to experimental viscosity data for a number of glassy materials, including SiO2, GeO2, B2O3, anorthite, diopside, xNa2O–(1-x)SiO2, xPbO–(1-x)SiO2, soda-lime-silica glasses, salol, and α-phenyl-o-cresol. Excellent fits of the equation to the viscosity data were obtained over the entire temperature range. The fitting parameters were used to quantitatively determine the enthalpies and entropies of formation and motion of configurons in the analysed systems and the activation energies for flow at high and low temperatures as well as fragility ratios using the Doremus criterion for fragility. A direct anti-correlation between fragility ratio and configuron percolation threshold, which determines the glass transition temperature in the analysed materials, was found