Publications by authors named "Gowtham Kuntumalla"
Article Synopsis
- Ultrasonic metal welding (UMW) is a solid-state joining technique with specific industrial uses but has a narrow operating range, making it sensitive to process variations.
- The study develops a machine learning-based response surface methodology (RSM) to better understand the complex relationships between welding parameters and joint quality, specifically focusing on optimizing peel and shear strengths.
- The findings demonstrate that techniques like Gaussian process regression (GPR) and support vector regression (SVR) outperform traditional polynomial models in prediction accuracy, and the research has broader implications for other manufacturing processes.
Heat conduction through bonded metal-polymer interfaces often limits the overall heat transfer in electronic packaging, batteries, and heat recovery systems. To design the thermal circuit in such systems, it is essential to measure the thermal interfacial resistance (TIR) across ∼1 µm to 100 µm junctions. Previously reported TIR of metal-polymer junctions utilize ASTM E1530-based two-block systems that measure the TIR by applying pressure across the interface through external heating and cooling blocks.
View Article and Find Full Text PDFScale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates.
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