The field of manufacturing and mechanical engineering is witnessing a significant shift towards data-driven approaches and predictive modeling. Researchers are increasingly leveraging machine learning and computer vision techniques to improve the accuracy and efficiency of various manufacturing processes, such as fatigue life prediction, geometry prediction, and assembly control. The development of novel datasets and benchmarks, such as those for rotary tube bending and articulated assembly motion prediction, is facilitating the training and evaluation of these models. Furthermore, the use of synthetic data pipelines and vision-based quality control systems is reducing the need for manual data collection and annotation, making these technologies more accessible to small- and medium-sized enterprises. Notable papers in this area include:

• A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures, which presents a machine learning-based pipeline for estimating fatigue life, reducing the need for costly simulations.
• DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction, which introduces a novel framework for predicting motion trajectories in mechanical assemblies.