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Item type:Publication, Access status: with files , Data-driven density prediction of AlSi10Mg parts produced by laser powder bed fusion using machine learning and finite element simulation(2023-09-26); ; ; Powder bed fusion of metals using laser beam (PBF-LB/M) is a commonly used additive manufacturing process for the production of high-performance metal parts. AlSi10Mg is a widely used material in PBF-LB/M due to its excellent mechanical and thermal properties. However, the part quality of AlSi10Mg parts produced using PBF-LB/M can vary significantly depending on the process parameters. This study investigates the use of machine learning (ML) algorithms for the prediction of the resulting part density of AlSi10Mg parts produced using PBF-LB/M. An empirical data set of PBF-LB/M process parameters and resulting part densities is used to train ML models. Furthermore, a methodology is developed to allow density predictions based on simulated meltpool dimensions for different process parameters. This approach uses finite element simulations to calculate the meltpool dimensions, which are then used as input parameters for the ML models. The accuracy of this methodology is evaluated by comparing the predicted densities with experimental measurements. The results show that ML models can accurately predict the part density of AlSi10Mg parts produced using PBF-LB/M. Moreover, the methodology based on simulated meltpool dimensions can provide accurate predictions while significantly reducing the experimental effort needed in process development in PBF-LB/M. This study provides insights into the development of data-driven approaches for the optimization of PBF-LB/M process parameters and the prediction of part properties.Publicationtype: Journal Article26 88 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Predicting melt track geometry and part density in laser powder bed fusion of metals using machine learningLaser powder bed fusion of metals (PBF-LB/M) is a process widely used in additive manufacturing (AM). It is highly sensitive to its process parameters directly determining the quality of the components. Hence, optimal parameters are needed to ensure the highest part quality. However, current approaches such as experimental investigation and the numerical simulation of the process are time-consuming and costly, requiring more efficient ways for parameter optimization. In this work, the use of machine learning (ML) for parameter search is investigated based on the influence of laser power and speed on simulated melt pool dimensions and experimentally determined part density. In total, four machine learning algorithms are considered. The models are trained to predict the melt pool size and part density based on the process parameters. The accuracy is evaluated based on the deviation of the prediction from the actual value. The models are implemented in python using the scikit-learn library. The results show that ML models provide generalized predictions with small errors for both the melt pool dimensions and the part density, demonstrating the potential of ML in AM. The main limitation is data collection, which is still done experimentally or simulatively. However, the results show that ML provides an opportunity for more efficient parameter optimization in PBF-LB/M.Publicationtype: Journal ArticleScopus© Citations 4 102 185