Federated Learning in Process Planning - Selection of Manufacturing Processes Using Federated Graph Learning

被引：0

作者：

Hussong, Marco ^{[1
]}

Klar, Matthias ^{[2
]}

Aurich, Jan C. ^{[2
]}

机构：

[1] Lehrstuhl für Fertigungstechnik und Betriebsorganisation (FBK), RPTU Kaiserslautern-Landau, Postfach 3049, Kaiserslautern

[2] FBK, RPTU Kaiserslautern-Landau

来源：

ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb | 2025年 / 120卷 / s1期

关键词：

Deep Learning; Federated Learning; Manufacturing Process Selection; Process Planning;

D O I：

10.1515/zwf-2024-0131

中图分类号：

学科分类号：

摘要：

Preparing of training datasets for deep learning in process planning presents a significant challenge. The data basis for various approaches has been based on synthetically created 3D models. However, such synthetic generation of training data only limitedly reflects industrial practice. Considering this background, approaches that can form a sufficiently large data basis from several companies'data without transferring the data to a central location are required. A promising approach in the described context is Federated Learning (FL). Therefore, this paper focuses on modeling an FL approach for process planning. © 2025 Marco Hussong, Matthias Klar und Jan C. Aurich, publiziert von De Gruyter.

引用

页码：269 / 273

页数：4

共 19 条

[1]

Klose G., Bornemann H., Thierstein J., Et al., Künstliche Intelligenz - Herausforderungen und Chancen für die rheinlandpfälzischen KMU, (2022)

[2]

Hussong M., Varshneya S., Rudiger-Flore P., Et al., A Process Planning System Using Deep Artificial Neural Networks for the Prediction of Operation Sequences, Procedia CIRP, 120, pp. 135-140, (2023)

[3]

Zhao C., Melkote S.N., Learning the Manufacturing Capabilities of Machining and Finishing Processes Using a Deep Neural Network Model, Journal of Intelligent Manufacturing, 35, 4, pp. 1845-1865, (2024)

[4]

McMahan B., Moore E., Ramage D., Et al., Communication-Efficient Learning of Deep Networks from Decentralized Data, International Conference on Artificial Intelligence and Statistics, 54, pp. 1273-1282, (2017)

[5]

Eversheim W., Organisation in der Produktionstechnik, Fertigung und Montage. Studium und Praxis, 4, (1989)

[6]

Industrielle Automatisierungssysteme und Integration - Produkt-datendarstellung und -austausch - Teil 21: Implementierungsmethoden: Klartext-Kodierung der Austauschstruktur, (2016)

[7]

Zhang Z., Jaiswal P., Rai R., FeatureNet: Machining Feature Recognition Based on 3D Convolution Neural Network, Computer-Aided Design, 101, pp. 12-22, (2018)

[8]

Ma Y., Zhang Y., Luo X., Automatic Recognition of Machining Features Based on Point Cloud Data Using Convolution Neural Networks, Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, pp. 229-235, (2019)

[9]

Shi P., Qi Q., Qin Y., Et al., A Novel Learning-based Feature Recognition Method Using Multiple Sectional View Representation, Journal of Intelligent Manufacturing, 31, 5, pp. 1291-1309, (2020)

[10]

Li L., Fan Y., Tse M., Lin K.-Y., A Review of Applications in Federated Learning, Computers & Industrial Engineering, 149, (2020)

← 1 2 →