Cyber Manufacturing

Research Area Description 

With the rise of digital transformation and cloud computing in the manufacturing industry, on-demand cyber manufacturing services have begun to emerge with the goal of connecting geographically distributed designers with manufacturers through online interactive design tools and instant job quotes. Present-day cyber manufacturing services (e.g., Mfg.com, Proto Labs, Xometry, Fictiv) have ignited the transformation of the manufacturing eco-system, but are also suffering from limited manufacturing capabilities, poor manufacturing quality and scalability. These challenges stem from the lack of a systematic method to capture highly variable and difficult-to-scale manufacturing know-how and “tribal knowledge” to produce a required product, which traditionally are acquired by human experts through years of experience.  

To this end, our efforts in Cyber Manufacturing as a Research Area aim to innovate and develop computational methods to capture critical manufacturing process capability knowledge from data to answer the following research questions: 1) How can we learn the relevant capabilities of manufacturing processes and resources (e.g., machines) from design and manufacturing data, and how effective is this learning? (2) With manufacturing knowledge derived from data, what methods can be used to automatically tailor or redesign products to guarantee manufacturability and enable automated process planning? These research questions are addressed by developing state-of-the-art computational tools using deep learning methods, which enable future evolution of cyber manufacturing services. 

On-going Projects  

Federated-Learning Based Manufacturing Capability Selection for Cyber Security in Cyber Manufacturing Services 

The emergence of platform-based manufacturing has substantially disrupted the way discrete parts are sourced and manufactured. However, the centralized business model of the manufacturing platforms has raised concerns for data ownership and cyber security of independent manufacturing suppliers. Contrary to centralized platforms, Cyber Manufacturing Service aims to connect designers with geographically distributed manufacturing suppliers by matching the design intentions with the manufacturing capabilities of candidate suppliers in a Cyber Manufacturing Marketplace. One of the key challenges in realizing the vision of Cyber Manufacturing Service is the lack of a computationally efficient mechanism for manufacturing capability search while maintaining data security of the proprietary datasets. In this work, we propose a federated learning-based deep unsupervised part retrieval model (FL-DUPR) to train a global embedding model without directly accessing proprietary datasets of suppliers. 

Select Publications   

Zhao, C., & Melkote, S. N. (2023). Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model. Journal of Intelligent Manufacturing, 1-21. 

Zhao, C., Dinar, M., & Melkote, S. N. (2023). Deep learning and sequence mining for manufacturing process and sequence selection. International Journal of Production Research, 1-22. 

Yan, X., Wang, Z., Bjorni, J., Zhao, C., Dinar, M., Rosen, D., & Melkote, S. (2023). Process-aware part retrieval for cyber manufacturing using unsupervised deep learning. CIRP annals. 

Yan, X., Williams, R., Arvanitis, E., & Melkote, S. (2024). Deep learning-based semantic segmentation of machinable volumes for cyber manufacturing service. Journal of Manufacturing Systems, 72, 16-25. 

Contact Information