Data-Driven Model for Prediction of Radiative Characteristics

Miguel Moreno Author

04/05/2021 Added

17 Plays

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Advancement of surface laser processing for thermal management applications via a combined deep learning and machine learning model that predicts surface patterns and thermal emissivity.

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[00:00:00.700]Hello everyone. My name is Miguel Moreno
[00:00:02.290]and this is Data-Driven Model for Prediction
[00:00:04.610]of Radiative Characteristics in collaboration
[00:00:06.241]with Andrew Reicks and Dr. Mohammad Ghashami, as my advisor.
[00:00:11.470]I need to start
[00:00:12.303]by first defining important background information.
[00:00:14.770]Like what emissivity is.
[00:00:16.560]Which in this case, is the potential
[00:00:18.150]of a surface to emit thermal energy in form
[00:00:20.640]of radiation, is given as a number from zero to one
[00:00:23.690]because it's a ratio to the black body radiation
[00:00:27.490]which is the theoretical limit.
[00:00:30.160]It's wavelength, angle and temperature dependent.
[00:00:33.610]There is a desire
[00:00:34.443]for materials with high terminal emissivity in industries
[00:00:37.370]like aerospace, photovoltaics or basically solar panels
[00:00:41.380]and also radiative cooling which is a promising new field.
[00:00:45.320]For this research,
[00:00:46.220]We want to focus on laser processed surfaces.
[00:00:49.430]So what is surface processing?
[00:00:51.786]It's goal is to optimize emissivity with surface patterns,
[00:00:56.070]but why does this improve emissivity?
[00:00:59.228]Well, microstructures increase surface area
[00:01:02.250]and act as "light traps"
[00:01:04.630]meaning they are locations where light concentrates
[00:01:09.640]and facilitates outgoing radiation.
[00:01:12.650]We can see this in nature, like in the case of silver ants
[00:01:16.320]whose hair act as tiny radiators.
[00:01:20.610]Like we can see here on the image on the right.
[00:01:24.730]We can also see in leaves and even in some butterflies.
[00:01:30.110]Some butterflies have micro structures in the wing
[00:01:33.320]that not only reflect the color,
[00:01:36.870]for example, vivid blue
[00:01:39.460]but also help them to regulate their temperature.
[00:01:42.670]The advantages of doing surface processing
[00:01:45.550]with laser specifically, is that it is low cost, scalable,
[00:01:50.270]easy to modify parameters, and it has no effect
[00:01:53.820]on material's bulk properties.
[00:01:56.280]Which means, for example, if we were to do this on a metal
[00:02:00.140]it would not affect its strength.
[00:02:02.493]Now focusing on our search research.
[00:02:05.920]Our objective was to predict emissivity
[00:02:08.230]of surface profiles to improve fabrication and
[00:02:11.450]development performance of laser process surface techniques.
[00:02:15.900]We can divide our research into three main parts.
[00:02:19.640]The first one being fabrication
[00:02:21.220]of samples with different surface patterns.
[00:02:23.520]Then we move on to the surface characterization
[00:02:26.790]or analysis of the surface.
[00:02:29.160]Now finally, the combined deep learning
[00:02:31.030]plus machine learning model.
[00:02:33.800]The fabrication part carried out
[00:02:35.440]by Andrew Reicks consists of 116 laser processed samples
[00:02:41.200]of polished aluminum alloy 6061
[00:02:44.180]using a femtosecond laser here
[00:02:46.010]at NERcF at UNL with changing parameters,
[00:02:49.910]such as peak fluence and pulse count
[00:02:52.080]to create different patterns in every sample.
[00:02:54.420]We can see here on the image in the right.
[00:02:57.890]An aluminum sample, with six different surface patterns
[00:03:03.630]which are the black squares.
[00:03:06.370]They don't have to be exactly one centimeter
[00:03:10.550]by one centimeter
[00:03:12.050]because it is not important for this case.
[00:03:16.870]The size at least.
[00:03:18.830]It was an open air fabrication
[00:03:20.240]which means there was no chamber involved.
[00:03:23.400]So the sample was in contact with the ambient air.
[00:03:27.260]We now move on to the surface characterization.
[00:03:30.420]We started by using a thermal imaging camera
[00:03:32.980]to measure the spectral directional emissivity.
[00:03:36.550]Which was measured at multiple angles
[00:03:38.560]from zero to 85, in five degrees increments.
[00:03:42.690]We then integrated these emissivity over all emission angles
[00:03:46.330]at a fixed wavelength and temperature,
[00:03:49.070]which in this case was 50 degrees.
[00:03:51.590]And then we obtained a spectral hemispherical
[00:03:54.010]emissivity value, which in the case
[00:03:56.040]of this surface is shown here as Eh
[00:03:59.610]which is equal to 0.956.
[00:04:02.540]Which is already way above the emissivity
[00:04:05.550]of normal aluminum.
[00:04:08.160]For the surface imaging,
[00:04:09.340]we use the Keyence Laser Scanning Microscope,
[00:04:12.540]also found here at NERcF.
[00:04:16.035]I then took three images per sample
[00:04:18.180]each from a different section to account
[00:04:19.830]for difference in pattern.
[00:04:22.020]And then I applied a height filter to
[00:04:25.012]facilitate further analysis,
[00:04:26.851]which is where we can see here on these images.
[00:04:29.670]The most orange means higher microstructure
[00:04:34.210]and the blue means shallow places.
[00:04:38.742]And we can already see how different the patterns
[00:04:41.401]can be ranging from a maximum height of 187.5 micrometers
[00:04:45.270]to all the way down to 17.9 micrometers.
[00:04:49.250]It is worth noting that they are all at 50X resolution
[00:04:53.230]which means we can appreciate the real difference
[00:04:57.320]in the patterns.
[00:05:01.170]I then used the VK-analyzer software
[00:05:03.585]which is from Keyence to obtain surface roughness parameters
[00:05:08.114]like the maximum height that I reported in the last slide.
[00:05:11.550]And after that, we take these processed images.
[00:05:16.400]So the images with the height filters, and we take them
[00:05:22.196]as input to a MATLAB model, which calculates the density
[00:05:27.021]of micro structures using a Fast Fourier Transform.
[00:05:29.260]This was carried out by Andrew Reicks.
[00:05:33.330]But basically how it works is that,
[00:05:36.702]it splits the image into three different layers
[00:05:41.477]and from each layer, it calculates the FFT.
[00:05:44.020]And after that we get three different density FFT values,
[00:05:48.020]which they are usually different,
[00:05:50.597]but at the end we take the one that makes more sense
[00:05:55.165]and we average them and we obtain the general density.
[00:05:59.870]The final part of this was the Data-Driven Model itself.
[00:06:06.460]So all these surface roughness parameters, all the images
[00:06:10.474]with the height filters, emissivity values
[00:06:13.604]are inputs for this model.
[00:06:16.420]And then we can obtain the prediction of patterns
[00:06:19.826]for a given emissivity.
[00:06:22.064]This graph shows in blue dots the experimental emissivity
[00:06:26.723]obtained from a given, from given patterns
[00:06:30.270]and the orange dots are predicted emissivities
[00:06:33.960]using this model.
[00:06:38.540]We can see how good this model is by looking at this graph,
[00:06:43.484]the percent error .
[00:06:44.610]We can see that almost all of it is below 8%
[00:06:50.782]with some outliers above 8%,
[00:06:55.857]but we believe
[00:06:56.961]or we know for sure that this model can be improved
[00:07:01.643]by increasing the number of samples.
[00:07:04.876]I then show two images of the same pattern
[00:07:08.410]but one is taken directly from the Keyence
[00:07:11.500]and the other one is a surface built by the model
[00:07:13.840]to obtain a given emissivity.
[00:07:17.279]In conclusion, this model demonstrates the power
[00:07:20.776]of combined deep learning plus machine learning
[00:07:23.457]for novel applications like radiative cooling.
[00:07:27.259]It helps in the advancement
[00:07:28.968]of laser processing knowledge within the field.
[00:07:31.632]And it opens a door
[00:07:32.465]for novel future applications, like radiative cooling
[00:07:35.878]which is going to be a our future focus.
[00:07:39.290]Thank you so much for your attention and have a great day.

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Data-Driven Model for Prediction of Radiative Characteristics

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