Abstract
The combination of distributed digital factories (D2Fs) with sustainable practices has been proposed as a revolutionary technique in modern manufacturing. This review paper explores the convergence of D2F with innovative sensor technology, concentrating on the role of Field Programmable Gate Arrays (FPGAs) in promoting this paradigm. A D2F is defined as an integrated framework where digital twins (DTs), sensors, laser additive manufacturing (laser-AM), and subtractive manufacturing (SM) work in synchronization. Here, DTs serve as a virtual replica of physical machines, allowing accurate monitoring and control of a given manufacturing process. These DTs are supplemented by sensors, providing near-real-time data to assure the effectiveness of the manufacturing processes. FPGAs, identified for their re-programmability, reduced power usage, and enhanced processing compared to traditional processors, are increasingly being used to develop near-real-time monitoring systems within manufacturing networks. This review paper identifies the recent expansions in FPGA-based sensors and their exploration within the D2Fs operations. The primary topics incorporate the deployment of eco-efficient data management and near-real-time monitoring, targeted at lowering waste and optimizing resources. The review paper also identifies the future research directions in this field. By incorporating advanced sensors, DTs, laser-AM, and SM processes, this review emphasizes a path toward more sustainable and resilient D2Fs operations.
Recommended Citation
L. Khan et al., "FPGA-Based Sensors for Distributed Digital Manufacturing Systems: A State-of-the-Art Review," Sensors, vol. 24, no. 23, article no. 7709, MDPI, Dec 2024.
The definitive version is available at https://doi.org/10.3390/s24237709
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
additive manufacturing; distributed digital factories; FPGA sensor; sustainability; traditional manufacturing; traditional sensors
International Standard Serial Number (ISSN)
1424-8220
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2024
PubMed ID
39686246
Comments
Intelligent Systems Center, Grant EEC 1937128