Influence of SiC nanoparticle contents on microstructural evolution and mechanical behavior of AZ91D magnesium matrix composites synthesised through a combination of a master pellet feeding technique and stir casting assisted by ultrasonic vibration

In this current study, an integrated approach involving a master pellet feeding technique and stir casting assisted by ultrasonic treatment processing was employed to fabricate AZ91D magnesium matrix nanocomposites reinforced with various concentrations of SiC nanoparticles (1.0, 1.5, and 2.0 wt%). The influence of the nanoparticle feeding method and the weight fraction of reinforcement on the microstructure and mechanical properties of AZ91D/SiC composites was thoroughly examined. The microstructural analysis revealed that the implementation of a master pellet feeding approach resulted in a relatively uniform dispersion of SiC nanoparticles within the primary α–Mg phase, whereas the original nanoparticle feeding method led to noticeable particle agglomeration in the microstructure. Additionally, with an increasing weight fraction of SiC nanoparticles, the primary α-Mg grain became finer and the β-Mg17Al12 intermetallic phase turned smaller. The hardness and tensile properties of AZ91D/SiC composites were significantly enhanced with increasing SiC contents. The addition of 2.0 wt% SiC reinforcements to the AZ91D magnesium alloy resulted in a maximal hardness increase of 41 %. Nevertheless, the ultimate tensile strength (UTS) and elongation (%EL) decreased when the SiC content exceeded 2.0 wt% due to the presence of increased porosity content and particle clusters in the microstructure. Notably, the AZ91D/1.5 wt% SiC composite exhibited promising tensile properties, with yield strength (YS), ultimate tensile strength (UTS), and elongation (%EL) values of 151 MPa, 192 MPa, and 4.54 %, respectively

Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data

The capability of extracting useful information from documents and further transferring into knowledge is essential to advance technology innovations in industries. However, the overwhelming majority of scientific literature primarily published as unstructured human-readable formats is incompatible for machine analysis via contemporary artificial intelligence (AI) methods that effectively discovers knowledge from data. Therefore, the extraction approach transforming of unstructured data are fundamental in establishing state-of-the-art digital knowledge-based platforms. In this paper, we integrated multiple Python libraries and developed a method as a cohesive package for automated data extraction and quick processing to convert unstructured documents into machine-interpretable data. Transformed data can be further incorporated with AI analytical methods. The output files have shown excellent quality of digitalised data without major flaws in terms of context inconsistency. All scripts were written in Python with functional modules providing easy accessibility and proficiency to achieve objectives. Eventually, the finalised well-structured data can be implemented for further knowledge discovery