Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization

arXiv:2402.17718v1 Announce Type: new
Abstract: Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mainly due to its layer-wise fabrication. A key issue is heat accumulation during DED, which affects the material microstructure and properties. While closed-loop control methods for heat management are common in DED research, few integrate real-time monitoring, physics-based modeling, and control in a unified framework. Our work presents a digital …

abstract additive manufacturing advantages arxiv bayesian challenges cs.lg digital digital twin eess.sp energy fabrication framework heat issue key layer machine machine learning manufacturing material optimization part process process optimization series time series twin type wise