Advanced manufacturing, additive manufacturing, structural health monitoring, wave propagation, ultrasonics NDE, mechanical vibrations, interfacial mechanics, systems integration, deep learning, microparticle adhesion and manipulation, microelectromechanical systems and cell mechanics
My research expertise lies in the broad area of system dynamics and vibrations with specialization in microparticle adhesion and manipulation using acoustic techniques coupled with laser interferometry. My research experience and interests also include additive manufacturing (AM), advanced, smart, and scalable manufacturing methods, elastic wave propagation, ultrasonic non-destructive evaluation, structural health monitoring (SHM), systems integration, big data analysis, image and signal processing, deep learning, and microelectromechanical systems (MEMS).
My current research focuses on advanced manufacturing, specifically focusing on defect detection in manufacturing, processing monitoring of metal additive manufacturing processes, computer vision, machine learning applications in manufacturing and engineering education (equitable assessments and longitudinal learning).
Some of my recent publications include:
Vallabh, Chaitanya Krishna Prasad, Haolin Zhang, David Scott Anderson, Albert C. To, and Xiayun Zhao. "Melt pool width measurement in a multi-track, multi-layer laser powder bed fusion print using single-camera two-wavelength imaging pyrometry." The International Journal of Advanced Manufacturing Technology (2024): 1-11.
Zhang, Zhi, Antony George, Md Ferdous Alam, Christopher Eubel, Chaitanya Vallabh, Max Shtein, Kira Barton, and David J. Hoelzle. "An additive manufacturing testbed to evaluate machine learning based autonomous manufacturing." Journal of Manufacturing Science and Engineering (2023): 1-17..
Akhavan, Javid, Jiaqi Lyu, Youmna Mahmoud, Ke Xu, Chaitanya Krishna Prasad Vallabh, and Souran Manoochehri. "Dataset of in-situ coaxial monitoring and print’s cross-section images by Direct Energy Deposition fabrication." Scientific Data 10, no. 1 (2023): 776.
Zhang, Haolin, Vallabh, Chaitanya Krishna Prasad, and Xiayun Zhao. "Machine learning enhanced high dynamic range fringe projection profilometry for in-situ layer-wise surface topography measurement during LPBF additive manufacturing." Precision Engineering 84 (2023): 1-14.
Zhang, Haolin, Vallabh, Chaitanya Krishna Prasad, and Xiayun Zhao. "Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF." Additive Manufacturing 58 (2022): 103075.
As a postdoctoral researcher at Pitt (2019 - 2022), I focused on (i) developing in-situ melt pool monitoring optical system for laser powder bed fusion (LPBF) – AM printing, (ii) photopolymerization 3D printing of polymers and polymer derived ceramics and (iii) developing manufacturing methods for patterning liquid metal alloys. For the LPBF – in-situ monitoring project, we aim to address the process monitoring of LPBF process via melt pool monitoring using a co-axial optical system integrated with high-speed camera (see: https://www.sciencedirect.com/science/article/pii/S1526612522002936 and https://patents.google.com/patent/US11874176B2/en). From the experimental data, we elucidate multiple melt pool metrics such as melt pool morphology and intensity changes over time, melt pool temperature. This in-situ data is validated with ex-situ measurements such as mechanical testing, X-Ray CT and microscopy. We have developed big data analytics for processing the high-speed image data. Currently we are working on integrating the big-data analytics with machine learning algorithms. These developed algorithms will be employed for anomaly detection based on the melt pool metrics and further predict the print part behavior using deep learning neural networks, developed based on both the ex-situ and in-situ data. Further, we are collaborating with NIST (NIST AMMD) for developing a unified AM database, which will aid the AM community in developing data-driven models and ML networks for detecting print anomalies based on our in-situ melt pool data and its signatures.
The second project focuses on developing experimental and materials system development for photopolymerization 3D printing, as a part of these studies we were awarded a PA manufacturing grant for developing ceramic filters. My contribution (50%) towards this proposal included literature review, method development and proposal drafting. Another project which I worked on focuses on the development of high-power laser-based method for patterning liquid metal (EGaIn) alloys (Vallabh et al, https://doi.org/10.1016/j.mfglet.2021.03.004).
During my first postdoc appointment at The Ohio State University, I worked on developing experimental system and methods for characterizing the mechanical properties of model biological cells using microelectromechanical systems (MEMS) devices. This technology will be further developed for characterizing the mechanical properties of cancer cells and understand cancer prognosis. The second project was focused on developing an autonomous manufacturing system using machine learning algorithms. We used Fused Deposition Modeling 3D printing as our prototype test case. For this work, I was responsible for developing test case scenario, experimental methods, and system integration.
My Ph.D. research was focused on microparticle adhesion and manipulation. My first project was to evaluate and assess the effect of electrostatic charge on toner adhesion (collaborated with Xerox Corp.). For the first time, we were able to predict the equivalent bulk charge on a single toner particle in a non-contact manner. Following which, I investigated the effect of surface temperature, relative humidity on microparticle adhesion. I have also designed and developed an experimental method to evaluate the adhesion energy distribution on the surface of a single microparticle using Surface Acoustic Wave (SAW) based particle rolling approach couple with laser Doppler vibrometry. More details about this work can be found at Vallabh et al, 2019, https://doi.org/10.1080/00218464.2019.1622094.
I was also a co-researcher on a project, which was focused on characterizing 3D printed phononic artifacts using ultrasonic acoustics and elastic wave propagation (Xu et al, 2017, https://doi.org/10.1115/1.4036908). We developed an experimental test platform which requires minimal human intervention during the test procedure. This testbed was further used for structural health monitoring of pharmaceutical tablets. For this work, I developed a system that integrates the hardware (steppers rails and other motors) with PC via LabVIEW software.
My research interest also lies in surface energy characterization of novel materials (such as liquid metal - EGaIn), surface energy characterization of thin films and substrates using acoustics techniques. I have experience in characterizing the surface energy of thin films/layers on electronic packaging chips for Intel Corporation using acousto-interferometry techniques and also characterizing the adhesive properties of functionalized substrates (such as silicon substrates functionalized with biotin).