313-593-5122

Applications:
Aerospace Engineering, Energy Research, Materials Science, Nanotechnology, Physics
Methodologies:
Bayesian Methods, Deep Learning, Machine Learning, Optimization
Relevant Projects:

NSF, Army Research Office (ARO), Oak Ridge Associated Universities (ORAU), ONR SBIR


Connections:

International Association for Computational Mechanics, American Society of Mechanical Engineers (ASME),The Minerals, Metals & Materials Society (TMS), Electrochemical Society (ECS)

Lei Chen

Assistant Professor

Department of Mechanical Engineering

Lei Chen’s group focus on applying data science tools to advanced manufacturing. Chen’s research expertise and interests are to integrate the physics-based computational and experimental methods and data-driven approaches, to exploit the fundamental phenomena emerged in advanced manufacturing and to establish the design protocol for optimizing the materials and process parameters of as-fabricated parts for quality control. Current research can be summarized by:
1 One of significant challenges in additive manufacturing (AM) is the presence of heterogeneous sources of uncertainty involved in the complex layer-wise processes under non-equilibrium conditions that lead to variability in the microstructure and properties of as-built components. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production, and current practice usually reverts to trial-and-error techniques that are very time-consuming and costly. This research aims to develop an uncertainty quantification framework by bringing together physical modeling, machine-learning (ML), and experiments.
2 Computational microstructure optimization of piezocomposites involves iterative searches to achieve the desired combination of properties demanded by a selected application. Traditional analytical-based optimization methods suffer from the searching efficiency and result optimality due to high dimensionality of microstructure space, complicated electrical and mechanical coupling and non-uniqueness of solutions. Moreover, AM process inherently poses several manufacturing constraints e.g., the minimum feature size and the porosity in the piezoelectric ceramics as well as at the ceramics-polymer interface. It is challenging to include such manufacturing constraints since they are not explicitly available. This research aims to develop a novel data-driven framework for microstructure optimization of AM piezoelectric composites by leveraging extensive physics-based simulation data as well as limited amount of measurement data from AM process.
3 Lithium (Li) and other alkali metals (e.g., sodium and potassium) are very attractive electrode candidates for the next-generation rechargeable batteries that promise several times higher energy density at lower cost. However, Li-dendrite formation severely limits the commercialization of Li-metal batteries, either because dendrite pieces lose electrical contact with the rest of the Li-electrode or because growing dendrites can penetrate the separator and lead to short circuits. This research aims to develop a computational model to accelerate the design of dendrite-free Li-metal batteries.

9.9.2020 MIDAS Faculty Research Pitch Video.

Blueprint for the research: data-driven modelling of additive manufacturing. Stereolithography-based and laser melting-based additive manufacturing processes are used to fabricate the powder-based piezoelectric ceramics and metals respectively, with controllable complex microstructures and/or architectures to tune material properties. Physics-based numerical simulations are performed in an “in-house” multiscale computational framework, which includes macroscopic finite-element based manufacturing process modelling, mesoscopic phase-field modelling of microstructure evolution and design, and first principles/CALPHAD calculation of thermodynamics and kinetics. Data-driven approaches include machine learning and uncertainty quantification with surrogate models, such as polynomial chaos expansion, Gaussian process, radial basis functions, etc.