Federated learning-based collaborative manufacturing for complex parts

The manufacturing of complex parts, such as aircraft structural parts and aero-engine casing parts, has always been one of the focuses in the manufacturing field. The machining process involves a variety of hard problems (e.g. tool wear prediction, smart process planning), which require assumptions, simplifications and approximations during the mechanism-based modelling. For these problems, supervised machine learning methods have achieved good results by fitting input–output relations from plenty of labelled data. However, the data acquisition is difficult, time consuming, and of high cost, thus the amount of data in a single enterprise is often limited. To address this issue, this research aims to realise the equivalent manufacturing data sharing based on federated learning (FL), which is a new machine learning framework to use the scattered data while protecting the data privacy. An enterprise-oriented framework is first proposed to find FL participants with similar data resources. Then, the machining parameter planning task for aircraft structural parts is taken as an example to propose an FL model, which mines the knowledge and rules in the historical processing files from multiple enterprises. In addition, to solve the data difference among enterprises, domain adaptation method in transfer learning is used to obtain domain-invariant features. In the case study, a prototype platform is developed, and to validate the performance of the proposed model, a data set is built based on the historical processing files from three aircraft manufacturing enterprises. The proposed model achieves the best performance compared with the model trained only with the data from a single enterprise, and the model without domain adaptation.