Mixture Experiments Using Machine Learning:
A “How-to” Approach
Marie Gaudard, Course Author
Senior Data Scientist and Statistical Consultant at Predictum Inc.
Philip Ramsey, Course Author
Senior Data Scientist and Statistical Consultant at Predictum Inc.
This is a shorter, method-centered version of “Design of Experiments for Mixtures using Machine Learning”.
1 day (delivered as 2 half-day sessions)
On-premises or virtual
Available on request
About this Course
Traditional Mixture DOE methods are not up to the challenge of today’s complex mixture systems, in which the components of interest are mixed and their proportions are optimized.
Traditional methods overburden users, assume inadequate models, and require too many experimental runs.
This 1-day, method-centered version of “Design of Experiments for Mixtures with Machine Learning” presents a revolutionary approach to the design of experiments for mixtures. It is based on modern machine learning methods that reduce sample size requirements, streamline the process of analyzing and modeling, improve accuracy, and provide deep insight for experiments of high complexity.
Upon completion, you will be able to use statistical software to perform mixture experiments and optimize mixture systems.
- Pharmaceutical and bio-engineering, conducting media or buffer optimization experiments for increased protein yields from bacteria or mammalian cells
- Semi-conductors, modeling yield on wafers, identifying yield loss mechanics, and investigating new wafer substrates
- Asphalt, improving asphalt mixtures for critical process attributes
- Metals manufacturing, optimizing critical characteristics in metallurgy
Introduction
- About the course
- Mixture Spaces, Components, and Blending
SVEM and Space Filling Designs
- A Machine-Learning Approach to Experimentation
- Case Study: Etch Rate (SVEM analysis)
- Exercise: Construct a Space-Filling Design
SVEM Analysis
- The SVEM Add-In
- Exercise: Conduct a SVEM Analysis (Pesticide)
Multiple Responses
- Case Study: Detergent
Bounds and Linear Constraints
- Case Study: Flare
Mixture of Mixtures Experiments
- Mixture of Mixtures Experiments
- Case Study: Harvey Wallbanger
- Exercise: Superfood Drinks
Mixture-Process Factor Experiments
- Mixture-Process Factor Experiments
- Case Study: Fly Ash
Instructors
Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute.
Marie holds a Ph.D. in Statistics from the University of Massachusetts at Amherst.
Phil is a Senior Data Scientist and Statistical Consultant at Predictum. He provides consulting services in data science, statistics, and machine learning for integrated analytical systems, custom projects, and training. He specializes in modern experimental design and analysis strategies and the use of statistics, data science, and machine learning in engineering and science.
In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA.
Phil holds a Ph.D. in statistics from Virginia Polytechnic Institute and State University.
Who Should Attend
Engineers, scientists, and researchers who work directly with mixtures, mixture designs, and mixture processes in various industries.
Duration
2 half-day sessions
Additional “office hours” with instructors available
Delivery Methods
On-premises or virtual
Prerequisites
No prior or formal training in design of experiments is assumed. Some familiarity with basic statistics is desirable.
JMP is required if you want to actively participate in the course. If you do not have JMP you can get a free trial version to install on your work or personal computer.
Pricing
Available on request
