Additive manufacturing technologies based on metal melting use materials mainly in powder or wire form. This study focuses on developing a metal 3D printing process based on cold metal transfer (CMT) welding technology, in order to achieve enhanced productivity. Aluminium alloy test specimens have been fabricated using a special 3D printing technology. The probes were investigated to find correlation between the welding parameters and geometric quality. Geometric measurements and tensile strength experiments were performed to determine the appropriate welding parameters for reliable printing. The tensile strength of the product does not differ significantly from the raw material. Above 60 mm height, the wall thickness is relatively constant due to the thermal balance of the welding environment. The results suggest that there might be a connection between the welding parameters and the printing accuracy. It is demonstrated that the deviation of ideal geometry will be the smallest at the maximum reliable welding torch movement speed, while printing larger specimens. As a conclusion, it can be stated that CMT-based additive manufacturing can be a reliable, cost-effective and rapid 3D printing technology with enhanced productivity, but without significant decrease in mechanical stability.
The kinetics of oxide layer formation on surface of Ti6Al4V alloy samples is a very important property especially if their application as medical implants is planned. Damaged protective surface layer usually heals in ambient condition however; during the self‐healing process toxic species can get into the surrounding living tissue. In our experiment the kinetics of the healing process proceeding at 3D printed alloy surface has been studied using electrochemical methods, among them scanning electrochemical microscopy. More than 40 min. time period was found long enough for total healing.
The methodology for delineating water bodies on multispectral remote sensing imagery was examined and evaluated. A supervised approach is tested with the aim to accurately detect inland water, moisturised soil surface and swampy patches on the Landsat TM 7 scene. The goal of this research is to investigate whether the application of remote sensing image interpretation could further refine the possibilities of future soil conductivity measurement research. The methodologies used were the application of supervised classification algorithms based on the training data collected in the area. The achieved overall classification accuracy value of 83.0795% suggests that the methodology could be used as a successful strategy to incorporate remote sensing data interpretation into soil conductivity measurement planning and application. The main conclusion that can be drawn is that processing of multispectral data with further refinement of the presented methodologies can led to very useful outcomes for environmental measurements.
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