Improving the quality of Chrysler crossmember castings
A crossmember is a structural component that undergoes strict X-ray inspection to ensure its quality. The optimal environmental and operational parameter settings are identified for making quality CHRYSLER crossmember castings through a novel optimization algorithm.
Reference: Y. Sun, G. Lin, Q. Han, D. Yang, C. Vian, Exploratory data analysis for achieving optimal environmental and operational parameter settings for making quality crossmember castings, Die Casting Congress & Exposition, in press, 2019.
Classification of machine functions
The increase in mobile machine automation and data collection has allowed mobile equipment manufacturers to push to implement their machines with smart machine learning algorithms to assist in the condition monitoring of the system.
Advanced machine learning algorithms are employed to classify the machine functions on a Bobcat 435 mini excavator.
Multifidelity learning for material properties prediction
We develop multi-fidelity model-based machine learning tools for empirical potential development for Si:H nanowires. The calculation speed using developed empirical potentials is fast compared to the first principle calculations with very good accuracy.
Deep learning for power system state estimation
The complexity of distribution power grids is increasing due to widespread deployment of renewable resources and power electronic devices. We employ deep belief network with non-Gaussian uncertainties for probabilistic state estimation of distribution power system.
Design optimal control strategy for ebola outbreak
The 2014-15 Ebola outbreak in West Africa is a serious threat to global public health. To design and evaluate different control strategies for Ebola outbreak, we employ machine learning, sensitivity analysis and parameter estimation to analyze the observation dataset. The results indicate that simultaneously strengthening contact-tracing and effectiveness of isolation in hospital would be most effective control strategies.
Robust data-driven discovery of physical laws
We develop a new machine learning approach on data-driven discovery of physical laws in implicit form from noisy datasets. This approach is effective, robust and able to quantify uncertainties by providing an error bar for each discovered candidate equations.