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This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing. [ABSTRACT FROM AUTHOR]

Ferritic stainless steel (FSS) is widely used to manufacture deep-drawn products for corrosion resistance applications, being the alloy drawability strongly affected by its microstructural anisotropy. This study combines a variety of microscopy techniques enabling in-depth analyses of the microstructural evolution of two different FSSs correlated to their deep drawing performance. One of the steels has a good correspondence with the standard EN-1.4016 (AISI 430). The other is a modified version of the previous one with higher contents of the ferrite-stabilising elements Si and Cr, and lower contents of the austenite-stabilising elements C, N, and Mn. Electron Backscatter Diffraction results confirm that the microstructural properties and drawability of FSS in the deep drawing process are improved in the modified steel version. Scanning transmission electron microscopy under low-angle annular dark field conditions evidences that the deformation mechanism of FSS during deep drawing follows a microstructural distortion model based on the grain size gradient and shows a variation of the deformation texture depending on the alloy composition. This work demonstrates the potential of advanced microscopy techniques for optimising the processing and design of ferritic stainless steels, with slight variations in the alloy composition, for deep drawing applications. [ABSTRACT FROM AUTHOR]

Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes. [ABSTRACT FROM AUTHOR]

Polylactic acid (PLA) is a biodegradable polymer that can replace petroleum-based polymers and is widely used in material extrusion additive manufacturing (AM). The reprocessing of PLA leads to a downcycling of its properties, so strategies are being sought to counteract this effect, such as blending with virgin material or creating nanocomposites. Thus, two sets of nanocomposites based respectively on virgin PLA and a blend of PLA and reprocessed PLA (rPLA) with the addition of 0, 3, and 7 wt% of titanium dioxide nanoparticles (TiO2) were created via a double screw extruder system. All blends were used for material extrusion for 3D printing directly from pellets without difficulty. Scanning electron micrographs of fractured samples' surfaces indicate that the nanoparticles gathered in agglomerations in some blends, which were well dispersed in the polymer matrix. The thermal stability and degree of crystallinity for every set of nanocomposites have a rising tendency with increasing nanoparticle concentration. The glass transition and melting temperatures of PLA/TiO2 and PLA/rPLA/TiO2 do not differ much. Tensile testing showed that although reprocessed material implies a detriment to the mechanical properties, in the specimens with 7% nano-TiO2, this effect is counteracted, reaching values like those of virgin PLA. [ABSTRACT FROM AUTHOR]

This paper studies the thermal, morphological, and mechanical properties of 3D-printed polylactic acid (PLA) blends of virgin and recycled material in the following proportions: 100/0, 25/75, 50/50, and 75/25, respectively. Real waste, used as recycled content, was shredded and sorted by size without a washing step. Regular dog-bone specimens were 3D printed from filaments, manufactured in a single screw extruder. Thermogravimetric analysis indicated that adding PLA debris to raw material did not significantly impact the thermal stability of the 3D-printed samples and showed that virgin and recycled PLA degraded at almost the same temperature. Differential scanning calorimetry revealed a significant reduction in crystallinity with increasing recycled content. Scanning electron microscopy showed a more homogenous structure for specimens from 100% pure PLA, as well as a more heterogeneous one for PLA blends. The tensile strength of the PLA blends increased by adding more recycled material, from 44.20 ± 2.18 MPa for primary PLA to 52.61 ± 2.28 MPa for the blend with the highest secondary PLA content. However, this study suggests that the mechanical properties of the reprocessed parts and their basic association are unique compared with those made up of virgin material. [ABSTRACT FROM AUTHOR]

In the new transformation of 'Industry 4.0', additive manufacturing technologies have become one of the fastest developed industries, with polylactic acid (PLA) playing a significant role. However, there is an increasing amount of garbage generated during the printing process and after prototypes or end-of-life parts. Re-3D printing is one way to recycle PLA waste from fused filament fabrication. To do this process successfully, the properties of the waste mixture should be known. Previous studies have found that PLA degrades hydrolytically, but the time at which this process occurs for 3D printed products is not specified. This work aims to establish the baseline of the degradation kinetics of 3D printed PLA products to predict the service time until which these properties are retained. To achieve this, 3D printed specimens were thermally and hydrothermally aged during several time intervals. Thermal and mechanical properties were also determined. This study reveals that tensile strength decreases after 1344 h of hydrothermal ageing, simulating 1.5–2.5 years of real service time. PLA therefore has the same thermo-mechanical properties before reaching 1.5-years of age, so it could be recycled. [ABSTRACT FROM AUTHOR]

Inclusion of spatial incoherence has been shown to give quantitative agreement between non-aberration-corrected high-angle annular dark-field scanning transmission electron microscopy images and theoretical simulations. Here we show that, using the same approach, a significant improvement in the correlation between calculated and experimental normalized integrated intensities is obtained in the InAsP ternary semiconductor alloy, but residual discrepancies remain. We have demonstrated, in good agreement with experimental intensities obtained in calibrated samples, that normalized integrated intensities show a low dependence on the sample thickness over a wide range of thickness values. This behaviour does not occur in conventional (non-aberration-corrected) images and constitutes a powerful tool for straightforward interpretation of high-resolution images in terms of atomic column-resolved compositional maps. [ABSTRACT FROM AUTHOR]

EN 1.4016 stainless steels combine good corrosion resistance with good formability and ductility. As such, their most popular applications are related to sheet forming. During re-crystallisation of Fe-Cr steels, deviations from the desired γ-fibre (gamma fibre, <111>||ND) texture promote a decrease in deep drawability. Additionally, α-fibre (alpha fibre, <110>||RD) has been found to be damaging to formability. In this study, an EN 1.4016 basic material and a modified one with optimised settings as regards to chemical composition and manufacturing process, in order to improve the formability properties, are characterised. The phase diagram, microstructure, Lankford coefficients and Electron Backscatter Diffraction (EBSD) (results confirm the evolution of texture during the processing of ferritic stainless steel. Texture is analysed by the interpretation of Orientation Distribution Function (ODF), using orientation density results for each sample obtained in the processing route. The cube ({001} <100>) and rotated cube ({001} <110>) textures dominate the crystal orientation from the slab until the intermediate annealing stage. After final annealing, there is a texture evolution in both materials; the γ-fibre component dominates the texture, which is much more intense in modified material supported by components that show good deep drawability, {554} <225>, and good transition from hot to cold rolling, {332} <113>. The modified composition and process material delivers a better re-crystallisation status and, therefore, the best drawability performance. [ABSTRACT FROM AUTHOR]

This work analyses the Bi incorporation in InAs1-xBix/InAs(100) epilayers grown by MBE through advanced transmission electron microscopy techniques. Samples grown from 350–400 °C resulted in Bi contents <3.3% exhibiting compositional variation in the growth direction. In contrast, roughly spherical clusters appeared in the sample grown at lower temperatures. The clusters are made up of the binary InBi with a tetragonal PbO phase surrounded by a matrix of InAs0.95Bi0.05 indicating the Bi solubility limit in InAs. These InBi crystals underwent a cubic distortion and are tilted 55° with regard to the InAsBi matrix. The crystallographic relationships are analysed in detail. [ABSTRACT FROM AUTHOR]

The distribution of bismuth in InAs1-xBix/GaAs quantum dots is analyzed by atomic-column resolution electron microscopy and imaging simulation techniques. A random Bi distribution is measured in the case of <0.03 ML/s Bi flux during the InAs growth with no significant variations in the shape or size of quantum dots, resulting in a low redshift and the degradation of the photoluminescence. However, for a 0.06 ML/s Bi flux the lateral indium segregation into the quantum dots is enhanced and Bi is incorporated inside them. As a result, a strong redshift and an increase of the peak intensity are found in this sample. [ABSTRACT FROM AUTHOR]

Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength ( Rm ), yield strength ( Rp ), hardness ( H ), and elongation ( A ) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

We determine the compositional distribution with atomic column resolution in a horizontal nanowire from the analysis of aberration-corrected high resolution Z-contrast images. The strain field in a layer capping the analysed nanowire is determined by anisotropic elastic theory from the resulting compositional map. The reported method allows preferential nucleation sites for epitaxial nanowires to be predicted with high spatial resolution, as required for accurate tuning of desired optical properties. The application of this method has been exemplified in this work for stacked InAs(P) horizontal nanowires grown on InP separated by 3 nm thick InP layers, but we propose it as a general method to be applied to other stacked nano-objects.