About

Jian Cao is the Associate Vice President for Research and the Cardiss Collins Professor of Mechanical Engineering at Northwestern University. She also holds courtesy appointments in Civil and Environmental Engineering, Industrial Engineering and Management Sciences, and Materials Science and Engineering. Her research interests focus on innovative manufacturing processes and systems, particularly deformation-based processes, laser additive, and laser subtractive processes. Her work has made fundamental contributions to understanding material behavior and the relationships between manufacturing processes and the performance of materials and parts. Her research integrates analytical and numerical simulation methods, control and sensors, design methodologies, and machine learning to advance manufacturing technologies. Prof. Cao's research group has designed unique manufacturing equipment for dieless sheet forming, microforming, and additive manufacturing, with current work impacting energy-efficient manufacturing, surface engineering, and distributed manufacturing. She has published over 500 technical articles, including about 300 journal articles, 10 book chapters, and approximately 20 patents. Recognized with numerous awards, she received the 2024 Hideo Hanafusa Outstanding Investigator Award from ASME & ISCIE, the inaugural ASME DeVor-Kapoor Manufacturing Medal, and the 2023 American Academy of Arts and Sciences Ted Belytschko Applied Mechanics Award, among others. She is a member of the National Academy of Engineering and has served in various professional roles, including editor-in-chief of the Journal of Materials Processing Technology and as a member of the National Materials and Manufacturing Board of the National Academies.

Research topics

• Computer Science
• Materials science
• Artificial Intelligence
• Physics
• Engineering
• Chemistry
• Mechanical engineering
• Political Science
• Mathematics
• Nanotechnology
• Optics
• Composite material
• Machine Learning
• Optoelectronics
• Organic chemistry
• Geology
• Algorithm
• Process management
• Environmental chemistry
• Economics
• Biology
• Environmental science
• Ecology
• Manufacturing engineering

Selected publications

• Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls

  npj Computational Materials · 2022 · 48 citations

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  Abstract In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds. The relationship between predicted cooling rate, microstructural features, and mechanical properties is examined, and cooling rate alone is found to be insufficient in giving quantitative property predictions. Because machine learning offers an efficient way to identify important features from series data, we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history. Very good predictions of material properties, especially ultimate tensile strength, are obtained using simulated thermal history data. To further interpret the convolutional neural network predictions, we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases. A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.

  PublisherOA PDFDOI

• Biofilm on microplastics in aqueous environment: Physicochemical properties and environmental implications

  Journal of Hazardous Materials · 2021 · 376 citations

  • Environmental science
  • Environmental chemistry
  • Ecology
  DOI

• Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

  npj Computational Materials · 2021 · 134 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.

  PublisherOA PDFDOI

• On the hot deformation behavior of Ti-6Al-4V made by additive manufacturing

  Journal of Materials Processing Technology · 2020 · 86 citations

  • Materials science
  • Metallurgy
  • Composite material
  PublisherDOI

• Opportunities and Challenges in Metal Forming for Lightweighting: Review and Future Work

  Journal of Manufacturing Science and Engineering · 2020 · 78 citations

  1st authorCorresponding
  • Computer Science
  • Political Science
  • Engineering

  Abstract The purposes of this review are to summarize the historical progress in the last 60 years of lightweight metal forming, to analyze the state-of-the-art, and to identify future directions in the context of Cyber-physically enabled circular economy. In honoring the 100th anniversary of the establishment of the Manufacturing Engineering Division of ASME, this review paper first provides the impact of the metal forming sector on the economy and historical perspectives of metal forming research work published by the ASME Journal of Manufacturing Science and Engineering, followed by the motivations and trends in lightweighting. To achieve lightweighting, one needs to systematically consider: (1) materials and material characterization; (2) innovative forming processes; and (3) simulation tools for integrated part design and process design. A new approach for process innovation, i.e., the Performance-Constraints-Mechanism-Innovation (PCMI) framework, is proposed to systematically seek new processes. Finally, trends and challenges for the further development in circular economy are presented for future exploration.

  DOI

• Mxene-modulated dual-heterojunction generation on a metal-organic framework (MOF) via surface constitution reconstruction for enhanced photocatalytic activity

  Chemical Engineering Journal · 2020 · 226 citations

  • Materials science
  • Chemical engineering
  • Nanotechnology
  DOI

• On the potential of recurrent neural networks for modeling path dependent plasticity

  Journal of the Mechanics and Physics of Solids · 2020 · 283 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  DOI

• Stable membrane candidate for deployable membrane space telescopes

  Journal of Astronomical Telescopes Instruments and Systems · 2020 · 3 citations

  • Materials science
  • Optics
  • Optoelectronics

  Larger mirrors are needed to satisfy the requirements of the next generation of UV–Vis space telescopes. Our study attempts to meet this requirement by demonstrating a technology that would deploy a large, continuous, high figure accuracy membrane mirror. The figure of the membrane mirror is corrected after deployment using a contiguous coating of a magnetic smart material (MSM) and a magnetic field. The MSM is a magnetostrictive material that is operable by magnetic write head(s), locally imposed on the nonreflective side of the membrane mirror. We report preparation, figure accuracy, stress analysis, and stability of the MSM coated CP1 polyimide substrate membrane mirror. The figure accuracy and magnetostrictive performance of the MSM coated membrane mirror are measured; furthermore, stability of the CP1 membrane for 48 h is observed and the results are found to be promising. In addition to membrane coating and the experimental procedure, the results of the surface profiling experiments are introduced and discussed.

  PublisherDOI

Recent grants

• Collaborative Research: Enhancing the Understanding of the Fundamental Mechanisms of Thermostamping Woven Composites to Develop a Comprehensive Design Tool

  NSF · $229k · 2003–2008

• 3D Near Field e-Writing with Submicron Resolution

  NSF · $310k · 2014–2018

• Incremental Forming at Multi-Scales

  NSF · $341k · 2007–2013

• GOALI: Process Analysis and Variation Control in Micro-stamping

  NSF · $300k · 2009–2013

• SGER/GOALI/Collaborative Research: Deformation Machining - A New Hybrid Process

  NSF · $76k · 2006–2008

Frequent coauthors

• Kornel F. Ehmann

  Northwestern University

  128 shared

• Zhaohui Yang

  34 shared

• Weiping Xiong

  Hunan University

  34 shared

• Rajiv Malhotra

  33 shared

• Wing Kam Liu

  31 shared

• M. P. Ulmer

  31 shared

• Meiying Jia

  27 shared

• Shuheng Liao

  23 shared

Labs

• Cao Research GroupPI

Education

• Ph.D., Mechanical Engineering

  Massachusetts Institute of Technology

  1995

• M.S., Mechanical Engineering

  Massachusetts Institute of Technology

  1992

• B.S., Automatic Control

  Shanghai Jiao Tong University

  1989

• B.S., Materials Science and Engineering

  Shanghai Jiao Tong University

  1989

Awards & honors

• 2024 Hideo Hanafusa Outstanding Investigator Award, ASME & I…
• Inaugural ASME DeVor-Kapoor Manufacturing Medal, 2023
• Ted Belytschko Applied Mechanics Award 2023 from the ASME Ap…
• 2022 Researcher to Know selected by Illinois Science & Techn…
• Member, National Academy of Engineering (2022)

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