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Argonne Scientists Use AI to Improve Airplane Manufacturing

When it comes to manufacturing new lightweight, yet strong components for new passenger jets, scientists are treating the process like trying to brew the most delicious cup of coffee. By using artificial intelligence (AI) and machine learning, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are intelligently and automatically selecting the perfect settings for a different kind of hot brew — the process of friction stir welding, a common ingredient needed to manufacture aeroplane components.

In a new collaboration, Argonne computer scientists are putting the power of the laboratory’s automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost.

This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist. Machine learning is a process by which a computer can train itself to find the best answers to a particular question.

The machine-learning algorithm uses a training dataset of various welding conditions and parameters from which aeroplane part properties can be determined. From this dataset, vastly more possible inputs are instantly analysed and ranked to determine which give the best possible components. Manufacturing aeroplane parts involves highly complex, sophisticated and expensive machines, and automating their manufacturing can save money and time, and improve safety and efficiency.

– Prasanna Balaprakash, Argonne Computational Scientist

DeepHyper automates the design and development of machine-learning-based predictive models, which often involve expert-driven, trial-and-error processes. Because no model is an absolute reflection of the truth. The researchers are not primarily trying to find the single best predictive model and the associated welding condition. Rather, they are generating hundreds of highly accurate models, combining them to assess uncertainties in the predictions, and then seeking to use these more tested predictions in the manufacturing process.

The team’s computationally intensive work is being enabled by supercomputing resources at the Argonne Leadership Computing Facility, a DOE Office of Science user facility. The partnership between Argonne, GE Research, EWI, and GKN Aerospace is funded by a grant from DOE’s Advanced Manufacturing Office. The project is entitled Probabilistic Machine Learning for Rapid Large-Scale and High-Rate Aerostructure Manufacturing.

Argonne National Laboratory (ANL) seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future.

As reported by OpenGov Asia, ANL is expanding its digital presence in science, technology, engineering and mathematics (STEM) education with two channels aimed specifically at students and teachers: STEAMville and a social media account that focuses on education. These new virtual programming initiatives, led by the laboratory’s Educational Programs and Outreach (EDU) department, will connect EDU and Argonne to STEM-driven students, teachers, and communities in Chicagoland and beyond.

One of the new platforms being explored by EDU is STEAMville, a combination of a social learning network and a learning management system. Northwestern University has developed the network over a decade to give schools, students, and science organisations their own virtual space to share and utilise STEM activities. This creates a rich catalogue of STEM programming that individuals and institutions can use and apply to their own programs, while also letting different groups and individuals interact with each other.

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