The field of geometric modeling and manufacturing is witnessing significant advancements with the integration of machine learning and artificial intelligence. Researchers are exploring innovative approaches to improve the accuracy and efficiency of various manufacturing processes, such as obsolescence forecasting, CAD reconstruction, and coverage path planning. Notably, the use of deep generative modeling and neural networks is becoming increasingly popular for tasks like shape primitive abstraction and etch depth prediction. These advancements have the potential to enhance process stability, manufacturing efficiency, and product quality. Noteworthy papers include: Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation, which proposes a novel framework for obsolescence forecasting using deep generative modeling. Point2Primitive, which introduces a CAD reconstruction network that produces editable CAD models from input point clouds by directly predicting extrusion primitives. In-situ and Non-contact Etch Depth Prediction in Plasma Etching via Machine Learning, which demonstrates the feasibility of using machine learning techniques for real-time etch depth prediction.