Digital Manufacturing
Assembly Line
A federated learning approach to automated and secure supplier selection in cyber manufacturing as-a-service
The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.
How Data-driven Manufacturing Unlocks Speed and Transparency during Injection Molding Process
Many of the parts we manufacture have at least one measurement that’s mission critical. Maybe the parts won’t work in an assembly unless a planned hole is within spec. Typical CAD models provide an opportunity to include specific dimensions, but what if you could tell your manufacturer early-on that dimension X is the one that makes or breaks a part? That’s where Critical-to-Quality (CTQ) comes in.
The CTQ specifications that you include in your quote and CAD model help to guide us during manufacturing, saving another critical dimension: TIME. We can often tell you if it’s possible for us to make your part before the mold is cut.
CTQ is also an important element of our digital manufacturing processes because we use these specs to evaluate initial runs of your parts. Let’s say that your parts require sample qualification or part validation. CTQ becomes even more crucial at that point because the data that flows from those initial shots can predict the future tolerances for those critical dimensions, revealing the suitability of end-use parts for a given assembly.
Digital Part Inspection Software Creates New Business Opportunities
Converting the shop to a digital inspection management system didn’t feel like an option but a necessity. “We have to evolve this capability or we will be left behind,” Bobby says. He knew that other shops had solved the inspection equation and felt confident that digital management was the solution to the shop’s bottlenecks. When speaking about the decision to purchase the shop’s first seat of the software, Bob says, “We liked auto ballooning and we also liked the data capture and reporting.” But these features were just the beginning; the capabilities of the software were far reaching and changed the culture of the shop.
Digital Transformation in Medical Device Manufacturing
The technique gained notoriety as a tool for creating “deepfake” videos on the internet, but it can also be adapted to work with 3D data to customize production of physical products, a concept that Goodfellow has dubbed “GANufacturing.” Glidewell is the first company to use GANs to make better teeth. Dentists often spend considerable time and effort creating custom dental prostheses. Not only does a new prosthesis have to fit a 3D shape that works with the patient’s other teeth, but it also must work well with the overall pattern of the person’s bite. As a result, a prosthesis typically needs to be tested on a dental model and ground to fit. Through GANufacturing, Glidewell can generate a near-perfect, realistic and functional tooth that needs little or no post-processing.
Inside Schneider Electric’s Smart Factory
According to Clayton, the goal of Schneider Electric’s IIoT initiative in Lexington is to boost efficiency and overall market competitiveness by introducing technologies that modernize and reinvent the control, monitoring and management processes of the plant.
It’s part of Schneider Electric’s global effort to digitally transform its factories and distribution centers. The 183-year-old company’s supply chain encompasses nearly 300 factories and logistics centers in more than 40 countries. Most of those facilities use the same IIoT technology that the company offers to its customers.
“These facilities are core to [our] Tailored Sustainable Connected Supply Chain 4.0 program, which creates a customized, sustainable and end-to-end connected supply chain across the plan, procurement, make, customer and sustain domains,” explains Clayton.
From Logs to Logging On: Paper Machines Built With Digital Manufacturing
ANDRITZ, an Austrian company that manufactures machinery for pulp and paper mills, is using digital manufacturing and artificial-intelligence (AI) processes to save millions of dollars. Skilled workers and engineers on ANDRITZ production lines are now able to take advantage of data-driven support as standard. 3D modeling and digital twins also give ANDRITZ a competitive advantage by guiding operators safely through maintenance and repairs and ensuring transparent access to data.