Ceramic Materials Short Courses
Increase Your Knowledge with ACerS Ceramic Materials Short Courses.
Choose your own journey
Are you an engineer, scientist, operations professional, or student looking to increase your materials science knowledge? Our short courses expand on foundational topics and will equip you with the skills required for today’s marketplace.
Continue your education by selecting one of our courses below.
Want more in-depth training on ceramic technology, manufacturing, and applications? Visit our online course page to check out the courses and training options.
This online course will introduce the five factors that control suspension rheology with examples.
This online course will provide an introduction to statistical process control (SPC) and then its application to ceramic processing
This online course will provide attendees with an introduction into the topic of additive manufacturing (AM) ceramics and learn more about the economical and technical aspects of this new technology.
This course is aimed at those interested in a high level, introduction to the intricate (dynamic and thermodynamic) processes that control crystal nucleation, growth and overall crystallization of glasses.
This online course will offer an introduction to machine learning and its application to glass science and engineering.
This online course will be offered over a 6 day period (90 minute sessions). The course will provide an introduction to the principles and practices in the sintering of ceramic materials.
Held prior to Ceramics Expo 2020
Dispersion and Rheology Control for Improved Ceramics Processing
with William M. Carty, Alfred University
Machine Learning for Glass Science and Engineering
Held in conjunction with GOMD 2020, this half-day course is targeted to students, scientists, or engineers who are interested in incorporating machine learning in their research or professional activities.
Topics will include
- General introduction to supervised and unsupervised machine learning,
- Review of existing methods and training of data-driven models,
- Development of composition-property predictive models in glasses by regression,
- Discovery of new glasses with improved properties by Bayesian optimization, and,
- Identification of relevant structural patterns in glass structures.
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