Materials Australia

We are pleased to share our new article has been published in The International Journal of Advanced Manufacturing Technology (IJAMT) entitled ‘A dimensionless group‑incorporating artificial neural network (DI‑ANN) model for single‑track depth prediction of SS316L for laser‑directed energy deposition (L‑DED)’. In this work, a dimensionless group-incorporating artificial neural network (DI-ANN) model was established to determine the relative influence of factors affecting single-track depth for laser-directed energy deposition (L-DED). The inputs of our DI-ANN model combined process parameters, process signatures acquired from in-situ monitoring, and multiphysics simulation-based dimensionless groups. The input features’ influence on single-track depth was analysed using Spearman’s correlation coefficient before training the DI-ANN model and was tested using a 5-fold cross-validation. The predictive quality of this model for single-track depth is 6.1% mean absolute percentage error (MAPE) and 11.7% maximum absolute percentage error (MXAPE). Compared with recent literature, this study shows an approximately 30% improvement in the MAPE and a 70% improvement in MXAPE. The presented model can be used for process parameter optimisation for porosity reduction and be a valuable foundation for feedback control algorithms for heat penetration. Congratulations to Jiayu and supervisors \

Moving to ANU It is with great pleasure that I am letting you all know that from February 2025 I will taking up the position of Director of the School of Engineering at Australia’s National University (ANU). I am looking forward to leading such an august School as well as working with wider college community within the College of Engineering, Computing and Cybernetics and the broader colleges in ANU.  The School has a strong and justified record of making significant advances in engineering and I hope to continue to deepen this. One of my major focusses will be on encouraging greater interaction with both community, government and industry in both our education and research. At this point it would be premature to define any precise refined or new directions as these will evolve in deep conversation with the school staff and the broader university.   Matched with my pleasure of being able to support ANU’s future is my sadness at leaving RMIT University. I joined RMIT as I believed it could be a great vehicle for translating research to the Victorian community and I look forward to this evolving in the future.  I will miss all my colleagues both from the ECP days and in the School of Engineering. Most of all I will miss my RDF team both staff, post doctoral fellows and students and close academic and industrial collaborators. It has been one of the great joys of my life to help you explore the leading edges of materials and process discovery in additive manufacturing, nano particles and corrosion protection. This has of course been facilitate by developing multi-scale and molecular modelling with leading partners. However, this is not a sad goodbye as I will remain involved supervising my students to completion. My other highlight at RMIT has been RMIT Europe. They are a creative and efficient bunch and have help me build deep collaborations. Again, I will continue these going forward.