Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories

Abstract

A crucial part of quality control in additive manufacturing (AM) is the decision to accept or reject parts based on their dimensional accuracy. Machine learning (ML) models can learn nuanced relationships between process parameters and resulting geometries; however, obtaining large quantities of supervised learning data to train ML models incurs non-trivial costs. Relevant measurement data may be available at other factories or stations within the same factory but cannot be pooled together for conventional centralized learning (CL) due to privacy constraints. Here, we propose federated learning (FL) that uses private data and information from distributed sources to train ML models that predict part dimensions and inform part qualification, thereby allowing collaborative model building without compromising privacy. We manufacture and measure 405 parts having three different designs and distribute them across three to 45 factories. The performance of FL is evaluated when factories produce similar parts, parts of different designs, and parts of different qualities. When factories produce similar parts or parts of different designs, FL predicts part geometry within 10 µm of the CL benchmark and within 15 µm of the process capability limit, with as few as nine parts at each factory. FL also outperforms individual learning where factories train on their own data by up to 96% in geometry prediction, thereby providing a win-win paradigm for privacy-preserving collaborative learning. When factories produce parts of different qualities, FL outperforms individual factories only when they produce less than 15 parts locally. We show that FL performance is influenced by two factors: the quantity of data in the federation and the statistical homogeneity of data across participating factories. Overall, this research demonstrates the promise of FL for privacy-preserving, data-efficient quality control and offers key insights to design data federations in scalable AM production.