Abstract: A method for predicting a material property of a component made by additive manufacturing includes: defining an area of interest and generating a mesh in the area of interest; providing a temperature model during the additive manufacturing process; providing a process parameter set; calculating a thermal history in the area of interest based on the process parameter set using the temperature model; and determining a solidification gradient and a solidification front velocity in the area of interest from the thermal history.

Abstract: A method for generating a test coupon specification for predicting fatigue life of a component includes determining a load condition for the component, providing a component design, and performing a strength analysis of the component design under the load condition determining a critical area of the component and a stress-related parameter of the critical area. The method includes providing a material condition of the component at least for the critical area of the component. To assist an end-user in determining which are optimal tests to be performed in order to obtain most relevant data for fatigue prediction of a specific component, the method also includes providing a material model and providing, as an input to the material model, the stress-related parameter, and the material condition. The material model generates, as an output, a test coupon specification for being tested in a testing machine.

Abstract: A computer-implemented method is provided for predicting a fatigue response of a material. The method includes receiving a user input specifying one or more surface roughness parameters that characterize a surface of a material for which fatigue life is to be predicted. The method further includes generating at least one realistic virtual surface profile from the specified one or more surface roughness parameters. The method further includes predicting fatigue life of the material in dependence of a stress field applied to the generated virtual surface profile. In accordance with specific embodiments, the prediction of the fatigue life may be carried out using finite element analysis based simulations, machine learning methods, or combinations thereof.

Abstract: A method and a system for fatigue life prediction of additive manufactured components accounting for localized material properties. The method and the system is employed for prediction of fatigue life properties of an additive manufactured element, with a data collection step in which several data points for maximum stress vs. cycles to failure for different given processing steps of the element are collected, with a training step in which a Machine Learning system is trained with the collected data, and with an evaluation step in which the trained Machine Learning system is confronted with actual processing steps and used to predict the fatigue life properties of the element.

Abstract: A method and a system for fatigue life prediction of additive manufactured components accounting for localized material properties. The method and the system is employed for prediction of fatigue life properties of an additive manufactured element, with a data collection step in which several data points for maximum stress vs. cycles to failure for different given processing steps of the element are collected, with a training step in which a Machine Learning system is trained with the collected data, and with an evaluation step in which the trained Machine Learning system is confronted with actual processing steps and used to predict the fatigue life properties of the element.