Journal article
Machine learning model for understanding laser superhydrophobic surface functionalization
Journal of manufacturing processes, Vol.69, pp.491-502
09/2021
DOI: 10.1016/j.jmapro.2021.08.007
Abstract
A general machine learning (ML) framework of surface wetting is proposed by considering a broad range of factors, including solid surface topography, solid surface chemistry, liquid properties, and environmental conditions. In particular, an XGBoost-based ML model is demonstrated for learning the surface wetting behaviors processed by a laser-based surface functionalization process, namely nanosecond laser-based high-throughput surface nanostructuring (nHSN). This is the first known attempt to apply machine learning to surface wetting by considering both surface topography and surface chemistry properties. Novel microscale and nanoscale topography parameters viz., roughness, fractal, entropy, feature periodicity are defined with suitable computer algorithms to comprehensively describe the surface topography. A novel set of surface chemistry parameters such as polarity, volume, and amount of functional groups are also used as the machine learning model input. Upon analyzing the importance of each parameter for the nHSN process, surface chemistry shows the greatest importance in determination of surface wettability, while surface morphology also plays a part in influencing the wettability.
•A machine learning model is firstly proposed for surface wettability considering both topography and chemistry.•Fractal dimension, 2D-entropy, and periodicity are used to describe surface morphology.•Polarity and functional groups are used to describe surface chemistry.•The results showed that the surface chemistry is the dominant feature while surface topography plays a supportive role.
Details
- Title: Subtitle
- Machine learning model for understanding laser superhydrophobic surface functionalization
- Creators
- Wuji Huang - University of IowaAvik Samanta - University of IowaYong Chen - University of IowaStephen Baek - University of IowaScott K Shaw - University of IowaHongtao Ding - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of manufacturing processes, Vol.69, pp.491-502
- DOI
- 10.1016/j.jmapro.2021.08.007
- ISSN
- 1526-6125
- eISSN
- 2212-4616
- Publisher
- Elsevier Ltd
- Language
- English
- Date published
- 09/2021
- Academic Unit
- Iowa Technology Institute; Industrial and Systems Engineering; Radiation Oncology; Chemistry; Mechanical Engineering
- Record Identifier
- 9984187073302771
Metrics
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