HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications on AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods and suggestions are given.

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Metal additive manufacturing (MAM) technology is now changing the pattern of the high-end manufacturing industry, among which MAM fabricated Ti6Al4V has been far the most extensively investigated material and attracts a lot of research interests. This work established a deep neural network (DNN) to investigate the grain boundary in competitive grain growth for a bi-crystal system, the column β grains of Ti6Al4V as an example. Because of the limited number of experimental samples, the DNN is trained based on the data coming from the Geometrical Limited criterion. A series of direct energy deposition experiment using Ti6Al4V is carried out under the Taguchi experimental design. The grain boundary angles between the column grains are measured in the experiment and used to evaluate the accuracy of DNN.