Optimising the manufacturing of a β-Ti alloy produced via direct energy deposition using small dataset machine learning.

Successful additive manufacturing involves the optimisation of numerous process parameters that significantly influence product quality and manufacturing success. One commonly used criteria based on a collection of parameters is the global energy distribution (GED). This parameter encapsulates the energy input onto the surface of a build, and is a function of the laser power, laser scanning speed and laser spot size.

Author Correction: Additive manufacturing of ultrafine-grained high-strength titanium alloys.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Additive manufacturing of ultrafine-grained high-strength titanium alloys.

Additive manufacturing, often known as three-dimensional (3D) printing, is a process in which a part is built layer-by-layer and is a promising approach for creating components close to their final (net) shape. This process is challenging the dominance of conventional manufacturing processes for products with high complexity and low material waste. Titanium alloys made by additive manufacturing have been used in applications in various industries.