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Kelvin Xie MSEN TAMU
Добавлен 15 май 2019
My name is Kelvin Xie and I am an Assistant Professor in the Department of Materials Science and Engineering at the Texas A&M University.
I am using this channel to share some of my teaching materials with students and other researchers who are interested in microscopy and materials science.
I am using this channel to share some of my teaching materials with students and other researchers who are interested in microscopy and materials science.
Kenna Ashen: AlphaFlow: autonomous discovery and optimization of multi-step chemistry...
AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning, led by Volk et al and published in Nature Communications in 2022.
Просмотров: 114
Видео
Derian Morphew: Martensite start temperature prediction through a deep learning strategy using ...
Просмотров 114Месяц назад
Martensite start temperature prediction through a deep learning strategy using both microstructure images and composition data, authored by Yang et al and published in Materials in 2023.
Joydeep Kundu: Deep learning for automated phase segmentation in EBSD maps
Просмотров 67Месяц назад
Deep learning for automated phase segmentation in EBSD maps - a case study in dual phase steel microstructures by Ostormunof et al. The work was published in Materials Characterization in 2021.
Merve Uysal Komurlu: Tribological characteristics of additively manufactured 316 stainless steels...
Просмотров 33Месяц назад
Tribological characteristics of additively manufactured 316 stainless steels using deep learning by Gupta et al. The work was published in Tribology International in 2023
Marcus Hansen: In-process monitoring of porosity during laser additive manufacturing process
Просмотров 43Месяц назад
In-process monitoring of porosity during laser additive manufacturing process was led by Zhang et al, published in Additive Manufacturing in 2019.
Luke Kruse: Finding the optical properties of plasmonic structures by image processing...
Просмотров 34Месяц назад
Luke Kruse: Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks by Sajedian et al. The work was published in Microsystems & Nanoengineering in 2019.
Chase Somodi - Learning physical properties of liquid crystals with deep CNN
Просмотров 140Месяц назад
Learning physical properties of liquid crystals with deep convolutional neural networks by Sigaki et al. The work was published in Scientific Reports in 2020.
Braden Miller - Development of Vickers hardness prediction models via microstructural analysis...
Просмотров 652 месяца назад
Development of Vickers hardness prediction models via microstructural analysis and machine learning by Swetlana et al, published in the Journal of Materials Science
Joydeep Kundu - Data-driven shape memory alloy discovery using artificial intelligence materials...
Просмотров 682 месяца назад
Data-driven shape memory alloy discovery using artificial intelligence materials selection (AIMS) framework. The paper was led by Trehern et al and was published in Acta Materialia in 2022.
Merve Uysal Komurlu - Comparative study of four supervised machine learning techniques...
Просмотров 652 месяца назад
Comparative study of four supervised machine learning techniques for classification. The paper was published by Mohamed in the Journal of Applied Science and Technology in 2017.
Eli Norris - Simple method to construct process maps for additive manufacturing using a SVM
Просмотров 483 месяца назад
The paper was published by Aoyagi, Wang, Sudo, and Chiba (Tohoku University) in Additive Manufacturing (2019).
Merve Uysal Komurlu - Unsupervised analysis of optical imaging data for discovery of reactivity...
Просмотров 833 месяца назад
Unsupervised analysis of optical imaging data for discovery of reactivity patterns in metal alloys, published by Li et al. in Small Methods (2023).
Marcus Hansen - Anomaly detection and classification in a laser powder bed additive manufacturing...
Просмотров 553 месяца назад
Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm, published by Scime and Beuth in Additive Manufacturing (2018).
Joydeep Kundu - Unsupervised learning-aided extrapolation for accelerated design of superalloys
Просмотров 383 месяца назад
The paper was published by Liao, et al. in njp Computational Materials (2024)
Maryam Ghotbi - Unsupervised learning implemented by Ti3C2-MXene-based memristive neuromorphic...
Просмотров 623 месяца назад
Unsupervised learning implemented by Ti3C2-MXene-based memristive neuromorphic system, published by Wan et al in ACS Applied Electronic Materials (2020).
Kaiji Zhao - Unsupervised machine learning via transfer learning and K-means clustering...
Просмотров 983 месяца назад
Kaiji Zhao - Unsupervised machine learning via transfer learning and K-means clustering...
Darien Morphew - Combinatorial synthesis and high-throughput characterization of microstructure...
Просмотров 623 месяца назад
Darien Morphew - Combinatorial synthesis and high-throughput characterization of microstructure...
Braden Miller - Identifying structural changes with unsupervised machine learning methods
Просмотров 664 месяца назад
Braden Miller - Identifying structural changes with unsupervised machine learning methods
Eli Norris - Unsupervised learning-aided extrapolation for accelerated design of superalloys
Просмотров 764 месяца назад
Eli Norris - Unsupervised learning-aided extrapolation for accelerated design of superalloys
High strain rate indentation response of NiCoV equi-atomic medium entropy alloy (TMS 2024)
Просмотров 1424 месяца назад
High strain rate indentation response of NiCoV equi-atomic medium entropy alloy (TMS 2024)
Reconstruct and index Auto CLAHE processed datasets
Просмотров 194Год назад
Reconstruct and index Auto CLAHE processed datasets
Crystallographic Variant Mapping Tutorial by Marcus Hansen
Просмотров 323Год назад
Crystallographic Variant Mapping Tutorial by Marcus Hansen
5.2: TRIP steel and shape memory alloys
Просмотров 790Год назад
5.2: TRIP steel and shape memory alloys
5.1: Martensitic transformation: characteristics, nucleation, and growth
Просмотров 1,8 тыс.Год назад
5.1: Martensitic transformation: characteristics, nucleation, and growth
4.2: TTT diagrams and phases in steels (pearlite, bainite, martensite, and more)
Просмотров 8 тыс.2 года назад
4.2: TTT diagrams and phases in steels (pearlite, bainite, martensite, and more)
TMS 2021 presentation on shear banding in cp-Ti
Просмотров 8422 года назад
TMS 2021 presentation on shear banding in cp-Ti
4.1: Diffusional transformation and spinodal decomposition
Просмотров 1,9 тыс.2 года назад
4.1: Diffusional transformation and spinodal decomposition
How to prepare specimen from bacteria and animal cells used in cell culture experiment?
Appreciate the detailed breakdown! A bit off-topic, but I wanted to ask: I have a SafePal wallet with USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). Could you explain how to move them to Binance?
Awesome lecture!
nice
very good videos! clearly summarized the book. interesting PED methods
I am currently a PhD student working on TEM in Singapore, your videos really helps a lot. recent work posted are also interesting
Sir Germanium should appear dark?beacuse of high atomic number ?
I have the same question
@trucquyenvothi6977 I got the a s
Very good video. Thank you!
This is a very interesting paper! Process parameters are expensive to optimize. SVM implementation definitely has the advantage of cost savings. It would be interesting to see if there are more novel strategies using similar designs. Is this a new class offered in MSEN?
Thanks, this is the best explanation ever for application of the eigenvalues.
OK but pearlite/spheroidite etc are structures/microstructures, not phases. Bit of a semantic thing I guess but phases are defined only by their atomic structure/composition, nothing above a few unit cells' length matters for the phase. But the same phase/phases can have very different properties depending on their larger scale structure.
Hi Kelvin, I was wondering why the steps are not "kinks" in 2:52? Thanks, Hanfeng
In my school, my professor taught me that is kink because it is the step in the dislocation line which remains "in the slip plane"
Hi Hanfeng, the kinks are dislocation segments on the slip plane, while jogs are not. Hope this answers your question.
I couldn't find website that you referred
Your explanation is very understandable and excellent sir, this video helped me a lot
Howdy
I have written you an mail, Please attend to it 🙏🙏🙏
Master could you tell me after you know the Z where the 0.392 from 🙇🏻♂️
7.02 in the video
Great Video with a lot of information............... Thank You So much
I thought austerity couldn't exist at a temperature below critical temperature, how is it existing at 300 degree Celsius
Hello Sir, How can I confirm if one phase has superlattice or not ? thank you
Thank you for the video, it was such a delight to watch.
what is the criterion for the screw dislocations, if we have a mixed dislocation, then the burgers vector is parallel to the g, so we still can find the dislocations in TEM right?
Thank you sir, for valuable lectures. can you please explain further how to measure the burgers vector?
Hello, do you still do research regarding phase transformations in metals?
quick question (3 years late) but in which scenario would you use the third case? the one e^(-as) laplace{f(t+a)}
thanks bro
ruclips.net/video/o7mIX_FqMC4/видео.html
Banger, thanks man
thx
I would like a more detailed demonstration of the Pierel stress, it is a doubt that I have had for a while :(
Thanks for your lectures. I see some worm-like pattern when I image a 5nm ferroelectric sample with HIM. With SEM, I only see the pattern when I use EsB detector. The inlens and SE detector doesn't see the pattern. What do you think is the contrast mechanism for the worm like pattern?
I love this lecture. Thank you very very much from Korea. Have a nice day~!
thanks a lot , this is one of the best explanation of the concept I have come across on RUclips.. 👍
nice and useful series, please upload more practical sessions.
what is the spot size ? we can control the aperture and condenser lenses to change the beam intensity and size (converge and diverge), then how the spot size is being controlled?
Well done! 👍
the least knobby dot to you too
Can you give me the link of website please?
I'm Vietnamese student thank you so much for sharing your knowledge
Thank you for your lectures. Unfortunately, I can't understand how to match the provided stress field diagrams with the 3D figures of the edge and screw dislocations. Could you please provide some additional sources where I can find the explanation or give me advice on how to understand that?
great lecture, but someone take this man out to the night club!
Hello Pfofessor, very beautiful analogy. Thank you for sharing. Best wishes.
Thanks for your helpful video. i am confused about the equation of calculating Si. the delta θ and θB is a angle parameter. why could we measure the distance (as marked in the video) to indicate the angle. what's the relationship between the θ and distance of fringe?
I'm confused, shouldn't you multiply S(theta) to the homogeneous critical energy instead of the heterogeneous one? SInce it's supposed to have higher critical gibbs energy
Tnx
very well explained , i am student of IIT VARANASI( BHU)
why enthalpy equals to 0 when the T equals to 25C or 298K?
Can you please explain the difference between gun shift and beam shift?
Do we have liquidus and solidus on ternary diagram?
Thank you so much! In the "angle of scattering ", I guess Elastically and inelastically should be vice versa
Kindly write good hand writing Sir its not clear for us