Application of machine learning in additive manufacturing of a novel Al alloy heat exchanger

Abstract Additive manufacturing (AM) of complex geometries faces limitations in the dimensional and geometrical accuracy, especially when the geometries are characterised by thin features designed to tailor the mechanical and thermal properties of novel heat exchangers (HXs). In this work, a novel, complex, thin hollow-walled lattice compact HX was fabricated using the Powder Bed Fusion-Laser Beam on Metal (PBF-LB/M) process. Given the intricate relationships between process parameters and complex design, machine learning (ML) methods were utilised to optimise the manufacturing workflow. Although new ML models would be required for different cases to ensure optimal performance, the flexibility of such approaches allows for recalibration and re-optimisation whenever there are changes to material properties, geometry, or manufacturing settings. A process map for the A205 Aluminium alloy was generated, investigating metallurgical defects and surface quality. Optimal process parameters for defect-free materials were estimated using a neural network (NN). Further, optimisation evaluated the influence of laser parameters, beam compensation (BC), and contour distance (CD), on the geometrical and dimensional accuracy of thin features, with a second NN predicting optimal BC and CD. Thickness deviations in hollow lattices were reduced to under 2%. A prototype of the novel HX using optimised parameters was successfully fabricated and characterised to evaluate manufacturing feasibility. The analysis of pores in thin features, potentially leading to leakage and part failure, was carried out through SEM analysis. While PBF-LB/M is well-established for HXs, this study demonstrates its capability for manufacturing highly complex, thin-walled designs when guided by ML-based optimisation. Graphical Abstract