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.

Feature-based translation of computer-aided design (CAD) models allows designers to preserve the modeling history as a series of modeling operations. Modeling operations or features contain information that is required to modify CAD models to create different variants. Conventional formats, including the standard for the exchange of product model data or the initial graphics exchange specification, cannot preserve design intent and only geometric models can be exchanged. As a result, it is not possible to modify these models after their exchange. Macro-parametric approach (MPA) is a method for exchanging feature-based CAD models among heterogeneous CAD systems. TransCAD, a CAD system for inter-CAD translation, is based on this approach. Translators based on MPA were implemented and tested for exchange between two commercial CAD systems. The issues found during the test rallies are reported and analyzed in this work. MPA can be further extended to remaining features and constraints for exchange between commercial CAD systems.

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