A review on in-situ monitoring, process control and residual stress prediction using machine learning methods in metal-based laser additive manufacturing

Key Points

• Residual stress prediction is crucial for maintaining the quality of metal-based additive manufacturing processes, and machine learning methods show promise.
• Key insights include the application of machine learning to enhance in-situ monitoring and process control during the additive manufacturing procedure.
• The review assesses various machine learning techniques applied to optimize process parameters in laser additive manufacturing.
• This highlights the need for innovative approaches to ensure precision and reliability in manufacturing processes.