Residual stress in metal additive manufacturing of thin-walled components: investigation and development of prediction models with respect to path planning

This dissertation focuses on the investigation of residual stress in metal additive manufacturing of thin-walled components. Residual stress is a critical issue in additive manufacturing processes as it can lead to structural deformations, reduced mechanical properties, and potential failure of the manufactured parts. The objective of this research is to develop a fast and accurate prediction model for residual stress using numerical and machine learning techniques, with a specific focus on path planning. To achieve the research objectives, the dissertation employs numerical simulations to understand the underlying physics and mechanics of residual stress formation in the additive manufacturing process. These simulations serve as a basis for generating training data and identifying the key factors influencing residual stress. The study explores the application of three machine learning algorithms: Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Random Forest Regressor. These algorithms are trained and evaluated using numerical data obtained from simulation of laser metal deposition (LMD), considering various thin-walled configuration components. The residual stress predictions obtained from the numerical models are validated through experimental measurements using the X-ray diffraction method. To begin, numerical simulations are performed to gain insights into the underlying physics and mechanics of residual stress formation in metal additive manufacturing. These simulations serve as a basis for generating training data and identifying the effects of path planning including deposition sequence and direction and geometrical features that influence residual stress. Furthermore, the dissertation investigates the role of path planning in mitigating residual stress in junction structures and thin-walled components. Various path planning strategies are explored using numerical models to identify optimal printing paths that minimize residual stress. The results of this research ...