About

Mihaela Banu is a Professor in the Department of Mechanical Engineering at the University of Michigan, where she also serves as the Associate Chair of Master and Doctoral Engineering. Her research focuses on lightweight materials, with an emphasis on developing micro- and nanocellulose composites, natural fiber composites, and associated manufacturing processes for automotive and aerospace applications. She is involved in multi-scale modeling of materials and the simulation of forming processes, as well as the manufacturing of personalized dental ligaplants. Banu's career has been marked by a progression from welding in a Romanian shipyard to pioneering advancements in lightweight materials at the University of Michigan. She has received numerous awards and recognitions, including the OVPR Collegiate Research Professorship and the Research Faculty Achievement Award, acknowledging her significant scholarly contributions and innovative work in her field.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Materials science
• Mechanical engineering
• Political Science
• Data Mining
• Composite material
• Acoustics
• Manufacturing engineering
• Structural engineering
• Industrial engineering
• Economics
• Data science
• Systems engineering
• Software engineering
• Geology
• Process management

Selected publications

• Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions

  Journal of Manufacturing Processes · 2022 · 100 citations

  • Computer Science
  • Data Mining
  • Computer Science
  DOI

• Numerical Simulation of the Mechanical Behavior of a Weft-Knitted Carbon Fiber Composite under Tensile Loading

  Polymers · 2022 · 15 citations

  Senior authorCorresponding
  • Materials science
  • Composite material
  • Structural engineering

  Knitted textiles are a popular reinforcement in polymer composites for their high drape properties and superior impact energy absorption, making them suitable for specific composite components. Nevertheless, limited attention has been paid to modeling the mechanical behavior of knitted fabric composites since knitted textiles generally offer lower stiffness and strength. This study presents a 3D finite element (FE) modeling of a precise geometrical model of weft-knitted carbon fiber thermoplastic composite to better understand its nonlinear mechanical behavior and interface damage mechanisms under tension. Toward this end, a representative volume element (RVE) of the weft-knitted fabric composite with periodic boundary conditions (PBCs) is generated based on actual dimensions. The validity of the textile RVE to represent the macroscopic behavior was evaluated prior to analyzing the composite. The effect of fiber tow/matrix debonding during tension on the mechanical behavior of the composite is investigated using the cohesive zone model (CZM). Finally, the predicted results of the mechanical behavior of the composite with and without considering the interface failure are compared with the experimental measurements. It is found that the fiber tow/matrix interfacial strength has a significant effect on the tensile performance of the knitted fabric composites, particularly when they are subjected to a large strain. According to the simulation results, the highest tensile performance of the composite is achieved when the interfacial debonding is prevented. However, considering the fiber/matrix debonding in the modeling is essential to achieve a good agreement with the experimental results. In addition, it is concluded that stretching the fabric before composite manufacturing can substantially increase the tensile stiffness of the knitted composite.

  PublisherOA PDFDOI

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

  Journal of Manufacturing Science and Engineering · 2020 · 78 citations

  Senior 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

• Online quality inspection of ultrasonic composite welding by combining artificial intelligence technologies with welding process signatures

  Materials & Design · 2020 · 88 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Materials science

  Ultrasonic welding is a joining technology suitable for carbon-fiber-reinforced thermoplastic (CFRTP) components because of its high throughput, and ease of automation. An effective online weld-quality inspection technology can promote the industrial application of ultrasonic composite welding. Literature focused on the quality inspection of ultrasonic composite welding is scarce. To address this, the present study proposes an online weld-quality inspection method for ultrasonic composite welding by combining artificial intelligence (AI) technologies with welding process signatures. The failure load in a tensile-shear test and the weld quality level (i.e., under weld, normal weld, and over weld) are predicted simultaneously using artificial neural network (ANN) and random forest (RF) models. Eight features consisting of the duration and energy at each welding stage are extracted from the process signatures as independent variables. The results indicate that both the ANN and RF models exhibit high prediction accuracies. The weld quality can be assessed comprehensively and reasonably by considering both the failure load and weld quality level. The findings of this study demonstrate the feasibility of online weld-quality inspection for ultrasonic composite welding.

  DOI

Frequent coauthors

• Alexandru Epureanu

  23 shared

• V. Marinescu

  18 shared

• Alan Taub

  University of Michigan–Ann Arbor

  16 shared

• Ionuţ Constantin

  14 shared

• Florin Bogdan Marin

  13 shared

• Tae Wha Lee

  Yonsei University

  11 shared

• Ankush Bansal

  10 shared

• Rana Dabaja

  9 shared

Awards & honors

• 2026 U-M Mechanical Engineering Spring Awards Banquet (2026)
• Impact Institutes Seed Stage funding (2026)
• UMOR Research Faculty Achievement Award (2019)
• Willie Hobbs Moore Achievement Award (2021)
• Collegiate Research Professorship (2022)

Similar researchers at University of Michigan

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• Emily Toth Martinh-95