Multi-objective design of multi-material truss lattices utilizing graph neural networks.

The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.

Scheduling optimisation of alcohol test sites.

Drink driving is an infamous factor in road crashes and fatalities. Alcohol testing is a major countermeasure, and random breath tests (RBTs) deter tested drivers and passersby (observers who are not tested). We propose a genetic algorithm (GA)-based RBT scheduling optimisation method to achieve maximal deterrence of drink driving.

A meshless computational framework for studying cold spray additive manufacturing including large numbers of powder particles with diverse characteristics.

Cold spray (CS) has emerged as an appealing additive manufacturing (AM) technique for producing or repairing individual components or entire structures. Compared to fusion-based AM technologies, cold spray additive manufacturing (CSAM) offers distinct advantages in the fabrication of components, while avoiding some melting/solidification-related issues such as phase transformation and oxidation. It involves intricate processes that pose significant challenges for numerical modeling, particularly when simulating the entire process at a large scale.

Laser Ablation Study of Cutting Ceramics with Consideration of the Beam Inclination Angle.

Silicon alumina nitride (SiAlON) and alumina toughened zirconia (ATZ) ceramics are applied for ceramic cutting tools to machine, e.g., cast iron, nickel base alloys and other difficult-to-machine materials.

Digitisation of metal AM for part microstructure and property control.

Metal additive manufacturing, which uses a layer-by-layer approach to fabricate parts, has many potential advantages over conventional techniques, including the ability to produced complex geometries, fast new design part production, personalised production, have lower cost and produce less material waste. While these advantages make AM an attractive option for industry, determining process parameters which result in specific properties, such as the level of porosity and tensile strength, can be a long and costly endeavour. In this review, the state-of-the-art in the control of part properties in AM is examined, including the effect of microstructure on part properties.