The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

The development of new materials has always been strictly related to the rise of new technologies and progressively efficient systems. However, cutting-edge materials might not be enough to ensure the effectiveness of a given product if the design guidelines used do not favor the specific advantages of this material. Polymeric composites are known for their excellent mechanical properties, but current manufacturing techniques and the relatively narrow expertise in the field amongst engineers impose the challenge to provide the most suitable designs to certain applications. Bio-inspired designs, supported by thousands of years of evolution of nature, have shown to be extremely profitable tools for the design of optimized yet structurally complex shapes in which the tailoring aspect of polymeric composites perfectly fit. Bearing in mind the current but old-fashioned designs of auto-parts and vehicles built with metals with little or no topological optimization, the present work addresses how biomimicry is being applied in the mobility industry nowadays to provide lightweight structures and efficient designs. A general overview of biomimicry is made regarding vehicles, approaching how the use of composite materials has already contributed to successful cases.

In this work, the feldspar used as a flux in typical porcelain was replaced by waste glass, and the resulting batch composition contained 50% kaolin, 25% quartz and 25% waste glass. The properties of this porcelain with glass powder (GP) were compared to traditional porcelain (TP), which utilizes feldspar. The samples were fired at different temperatures, ranging from 1200 °C to 1400 °C. Technical parameters, such as water absorption, modulus of rupture and K IC, phase analysis by XRD, and microstructure by SEM were analysed. The results indicated that the lower fracture toughness and MOR of GP is a consequence of the peculiar microstructure of this porcelain. The reduction in firing temperature and the use of a cheaper substitute for feldspar makes GP also an attractive economical alternative.