Research Projects

• Additive Manufacturing of Frequency Selective Surfaces 

  Faculty Mentor: Prof. Srikanthan Ramesh 
  Disciplines: Industrial Engineering, Additive Manufacturing, Printed Electronics 

  Frequency selective surfaces (FSS) are periodic arrays of conductive patterns that control the transmission and reflection of electromagnetic waves at targeted frequencies. They are widely used in antennas, radomes, stealth coatings, and wireless communication systems. Conventional fabrication methods such as photolithography and etching are costly, rigid, and poorly suited for conformal or large-area applications. This project explores additive manufacturing, specifically aerosol jet printing (AJP), to directly fabricate metallic FSS patterns on planar and curved substrates. Site participants will design and simulate repeating unit cells, translate them into printable designs, and manufacture them using AJP with conductive nanoparticle inks. Students will investigate how process parameters affect the morphology, conductivity, and dimensional fidelity of printed features, and will evaluate electromagnetic performance through impedance and S-parameter measurements in the RF/microwave regime. The project will also involve optimization of printing parameters and pattern geometries to balance manufacturability, electrical performance, and material efficiency. As part of on-going collaborative work with NASA, participants will also gain exposure to system-level considerations of FSS integration for aerospace applications and will have opportunities to participate in technical meetings with NASA researchers. Training will include hands-on experience with aerosol jet printing, profilometry, microscopy, and simulations. The results will provide insight into scalable fabrication of next-generation FSS devices with applications in communications, sensing, and space systems. 

• Simulation and Estimation of Mechanical Properties of Additively Manufactured Metal Components 

  Faculty Mentor: Prof. Ranji Vaidyanathan 
  Disciplines: Materials Science and Engineering, Additive Manufacturing, Computational Modeling 

  Challenges in additive manufacturing persist in testing, qualification, and certification of metallic parts, which are expensive and time-consuming due to anisotropy, residual stresses, and defects introduced during processing. These issues limit adoption of AM for primary load-bearing components. This project will focus on correlating thermal history, grain size, and local mechanical behavior in additively manufactured metals produced by Laser Powder Bed Fusion and powder-based Directed Energy Deposition. Samples provided by Virginia Tech and North Carolina A&T will be sectioned using EDM and characterized by Electron Back-Scattered Diffraction (EBSD) to determine grain size and solidification features. Ansys simulations will be employed to connect grain morphology to local yield stress predictions. Micro-tensile testing will generate location-specific modulus and fatigue properties, enabling the construction of global stiffness and fatigue property matrices. By applying boundary conditions, component lifetime can be estimated. This approach, linking grain size to thermal history and mechanical performance, offers a novel pathway to accelerate qualification of 3D printed metals. Students will gain experience in advanced materials characterization, finite element simulation, and mechanical property testing, contributing to new methods for predicting the performance and lifetime of AM components.

• 3D Bioprinting of Microcapillaries for Tissue Models 

  Faculty Mentor: Prof. Sundar Madihally 
  Disciplines: Chemical and Biological Engineering, Additive Manufacturing, Materials Characterization 
  Additive manufacturing using 3D bioprinting has gained significant attention in biomedical engineering, particularly for fabricating vascularized tissue models such as skin, liver, and blood–brain barrier systems. However, most printed vasculature exceeds 0.25 mm in diameter, while native tissue vasculature is typically smaller than 0.20 mm. To address this challenge, our group has developed low-cost (<$150) 3D bioprinters that employ sterile syringes, custom-built nozzles, and open-source software. These systems enable control of printed structures through adjustment of flow rates and hydrogel compositions, and a range of biocompatible hydrogel mixtures have been tested. Site participants will contribute to advancing this bioprinting platform through one of several possible directions: (i) improving the design of the bioprinter by simplifying tubing arrangements, adding additional printheads, or reducing cell requirements for vascular models; (ii) investigating the influence of hydrogel polymer combinations on vascular cell behavior; or (iii) exploring the encapsulation of drugs within hydrogels for controlled delivery during printing. Students will receive training in bioprinting and related experimental techniques, with projects tailored to their interests. 

   

• Engineering Self-Lubricating Nanocomposites by Cold Spray Additive Manufacturing 

  Faculty Mentor: Prof. Pranjal Nautiyal 
  Disciplines: Mechanical and Aerospace Engineering, Additive Manufacturing, Materials Characterization 

  Cold spray additive manufacturing (CSAM) is an emerging solid-state deposition technique in which heated high-pressure gases (e.g., air, N₂, He) accelerate fine particles to supersonic speeds through a de Laval nozzle. Upon impact with a substrate, the particles undergo severe plastic deformation and bond without melting, thereby preserving desirable microstructural features and avoiding solidification defects. This project focuses on leveraging CSAM to engineer self-lubricating metal matrix nanocomposites by incorporating two-dimensional (2D) nanomaterials such as graphene, h-BN, and MoS₂ into ductile metal matrices. Site participants will investigate the mechanisms of impact bonding between 2D nanofillers and metallic powders, studying how process parameters influence microstructure and mechanical performance of the resulting nanocomposites. Students will further evaluate the self-lubrication behavior of the coatings through tribological testing, examining the activation of lubrication pathways under sliding contact. Training will include hands-on experience with cold spray deposition, scanning electron microscopy, metallography, nanoindentation, and in-situ tribometry. The findings will provide insights into scalable fabrication of advanced functional coatings with applications in aerospace and energy systems. 

• Collaborative Design Optimization for Additive Biofabrication 

  Faculty Mentor: Prof. Akash Deep 
  Disciplines: Industrial and Systems Engineering, Additive Manufacturing 
  Additive biofabrication enables precise, layer-by-layer construction of biological tissues, but the inherent design complexity and variability present significant barriers to efficient development of patient-specific products. This project explores computational approaches to support collaboration between biofabrication facilities, allowing knowledge sharing to reduce design iteration cycles and accelerate product development. Site participants will engage in literature review, computational modeling, and simulation to identify strategies for optimizing design parameters and evaluating performance across different biofabrication scenarios. Students will also investigate methods for integrating design inputs from multiple sources and analyze how variability impacts product performance. The project provides training in computational modeling, data analysis, and design optimization in a biomanufacturing context, with the goal of advancing collaborative frameworks for patient-specific tissue engineering. 

   

• Mechanical Testing of Uncured Prepreg Tape Composite Material 

  Faculty Mentor: Prof. Wei Zhao 
  Disciplines: Mechanical and Aerospace Engineering, Composite Manufacturing, Machine Learning 

  Automatic fiber placement (AFP) is a widely used technique for fabricating composite structures, but the processing of uncured prepreg tape is strongly influenced by temperature, compaction force, and laydown speed. This project focuses on experimentally characterizing the bending, shear, and tack behavior of slit-tape laminates under varying AFP conditions. Bending properties will be measured using a modified beam bending test in an oven across 20–80 °C, while in-plane shear properties will be determined using a 10° off-axis tensile test with a universal testing machine. These experiments will capture the viscoelastic response of prepreg tape at different strain rates and temperatures. Site participants will gain hands-on training in mechanical testing, thermal and mechanical characterization, and data analysis. In parallel, they will contribute to the development of a machine learning neural network model that predicts prepreg tape properties and tack behavior under different AFP process conditions. The results are expected to improve predictive modeling of AFP processes, ultimately supporting more reliable and efficient composite manufacturing. 

• Deep Learning and Explainable AI for Anamoly Detection and Threat Mitigation in Smart Manufacturing

  Faculty Mentor: Prof. Sharmin Jahan 
  Disciplines: Computer Science, Cybersecurity, Smart Manufacturing 
  Smart manufacturing relies on interconnected systems to optimize production, but this interconnectivity also creates vulnerabilities to cyber threats. Anomaly detection is a common strategy for identifying such threats, yet traditional machine learning models often struggle with the high-dimensional and heterogeneous data produced by manufacturing systems. Deep learning (DL) offers greater capacity to analyze this data, but its “black box” nature limits interpretability and adoption for security-critical applications. This project investigates the integration of explainable artificial intelligence (XAI) with DL to improve transparency, enabling stakeholders to better understand security vulnerabilities, patterns, and conditions in real time. Site participants will explore DL models for anomaly detection, apply XAI techniques to interpret results, and implement a procedural framework to assess threats in smart manufacturing environments. Students will conduct experiments using open-source 3D printing simulators, introducing controlled anomalies and evaluating the effectiveness of the proposed framework. Through this work, participants will gain training in cybersecurity, deep learning, and explainable AI, while contributing to the development of adaptive, interpretable security strategies that help mitigate evolving cyber risks in manufacturing. 

   

• Developing a Fuzzy Signal Detection Model for Visual Inspection Tasks in Manufacturing 

  Faculty Mentor: Prof. Katherina Jurewicz 
  Disciplines: Industrial Engineering, Human Factors, Manufacturing Systems 
  High-quality manufacturing requires extremely low defect rates, making visual inspection a critical step often performed by human operators. Traditional signal detection theory (SDT) has been used to model human decision-making in inspection tasks, but it assumes binary responses (hit, miss, false alarm, correct rejection). In practice, uncertainty in inspection environments means that human judgments are often less clear-cut. Fuzzy signal detection theory (FSDT) extends SDT by incorporating fuzzy logic, allowing for nonbinary responses and providing a more realistic representation of human performance under uncertainty. Despite its promise, FSDT has rarely been applied in manufacturing inspection contexts. Site participants will investigate the use of FSDT to model visual inspection performance in manufacturing. Students will design and analyze inspection experiments, apply statistical modeling to characterize human performance, and compare the predictive power of FSDT versus traditional SDT. In addition, students will explore how AI-enabled decision aids can complement human inspectors, contributing to a better understanding of human–automation interaction in manufacturing. Through this work, participants will gain experience in human factors research methods, statistical modeling, and AI integration for quality assurance. 

   

• Establishing the Microstructure and Properties of Directed Energy Deposited SS 316L 

  Faculty Mentor: Prof. Sandip Harimkar 

  Disciplines: Materials Science, Mechanical Engineering, Additive Manufacturing 

  Additive manufacturing has emerged as a key process for fabricating complex components and repairing damaged parts, offering unprecedented control over processing parameters to tailor material properties. However, the microstructure, defect formation, and mechanical response of alloys remain difficult to predict due to the complexity of laser-material interactions. This project will investigate the microstructure and property development in stainless steel 316L fabricated using directed energy deposition (DED). Site participants will vary process parameters systematically and characterize the resulting deposits using microscopy, X-ray diffraction, and mechanical testing. The data will be used to build predictive frameworks for microstructure and mechanical performance using design-of-experiments and machine learning approaches. Training will provide hands-on experience with metal additive manufacturing, advanced materials characterization, and data-driven analysis, contributing to improved understanding of DED process-structure-property relationships.