Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing
Hyunwoong Ko, Yan Lu, Paul W. Witherell, Ndeye Y. Ndiaye
Additive manufacturing (AM) assisted by a digital twin is expected to revolutionize the realization of high-value and high-complexity functional parts on a global scale. With machine learning (ML) introduced in the AM digital twin, AM data are transformed into knowledge continuously and automatically; hence AM products will be designed and manufactured with improved quality. Conventional product development systems, however, fail to fully adopt ML algorithms on the increasingly available AM data, which could directly contribute to a stagnant AM knowledge acquisition. To address the limitation, this paper proposes an algorithmic framework for constructing AM knowledge automatically and continuously from data. The proposed algorithm develops predictive models to correlate process parameters with part structure and properties using ML algorithms on design, process control, in-situ monitoring, and ex-situ measurement AM data. Based on the predictive models, the algorithm constructs prescriptive rules necessary for decision-making in AM product developments. The constructed rule knowledge is stored in a knowledge base by the algorithm for further AM knowledge query and constructions. Then, the algorithm provides feedback to a knowledge-query formulation phase, which forms the algorithm into a closed loop. Through the algorithm, AM knowledge can self-evolve continuously, while reflecting dynamically generated AM data in an automated and autonomous manner. A case study is presented to illustrate the proposed algorithm and a software prototype is developed to demonstrate the algorithm capability.
CASE 2019 - International Conference on Automation Science and Engineering