Develop manufacturing technology and value for difficult-to-machine superalloy materials, fully automated machining technology, improve efficiency and machining quality stability, and combine automation and intelligence for unmanned autonomous operation of machining and inspection.

A self-improving thermal error compensation model is established by federated learning to improve the machine tool performance under different ambient temperatures. Each machine can share and update the latest model parameter to improve the initial model from different ambient temperature variation trails, but still keep the private ambient information of each machine.

The NeRF-based technique describes a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.

This project aims to develops a completely manufacturing flexible decision framework by applying big data analytics and AI techniques that integrates data from different decision-making units such as APC, APS, capacity planning, and inventory management planning to increase the decision quality and resilient capability in uncertain risks based on the perspective of factory operation.