Webinars
Stat-Ease is proud to offer free webinars to those interested in design of experiments (DOE) and Design-Expert® software. Presented on a regular basis, topics range from beginner to advanced levels. Material may be new or drawn from our ever-popular DOE workshops.
If there is a particular subject you are interested in and don't see below, send us an email and let us know. If there is enough interest, we may present your topic in a future webinar.
To stay up-to-date on new webinars, sign up for our mail list. We announce all of our new and upcoming webinars to our email list. If you are worried about too much email, sign up for The DOE FAQ Alert. This newsletter comes out every other month and contains what you need to stay current on Stat-Ease and our events.
"Thank you very much for the continuing education opportunities through Stat-Ease's webinar offerings. The instructors do an excellent job of explaining DOE. Thank you, Stat-Ease!"
—Jeff Reimer, Principal Scientist, Sigma-Aldrich Corporation
Upcoming Live Webinars:
Rollback the covers on the incredibly useful optimization tools provided by Design-Expert® software (DX). Discover how DX manipulates multiple response-models to search out the most-desirable sweet spot. Master the controls for setting goals, changing relative importance, and many other options that lead to an optimal outcome. After this webinar, you will be far ahead for making the most from every experiment.
Tuesday Aug 18 at 10am Central Time
Richard Williams, Stat-Ease Consultant, details what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools. Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM) for process optimization and last, but not least, a look into mixture design for optimal formulation.
Date: Aug 20, 2020
Time: 1:00pm ET/12:00pm CT/11:00am MT/10:00am PT
Discover the secrets to customizing your experiments using optimal designs. When standard (canned) response surface designs are unsuitable due to operational or physical constraints, optimal (custom) designs may be the answer. During this webinar, gain a greater understanding of the statistical and practical differences between D and I-optimality. Learn the importance of considering multiple criteria when building an optimal design, such as adding lack of fit points and replicates. All these issues are considered at a practical level – keeping the actual experimenters in mind. This brings to the forefront such considerations as subject matter knowledge (first principles), factor choice, and the feasibility of the experiment design.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday September 16, 2020
Time: 10:00am Central US Time
Recorded Webinars:
Richard Williams, Stat-Ease Consultant, details what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools. Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM) for process optimization and last, but not least, a look into mixture design for optimal formulation.
Design of Experiments (DoE) facilitates the screening for relevant factors and the optimization of the latter in terms of one or several responses. The number of factors that can be included in screening designs is substantial, but may still be a bottleneck if complex processes with interdependent steps have to be optimized.
Here we present an iterative approach to structure the multi-parameter problem and illustrate how challenges regarding the experimental implementation of a design can be handled.
[Slides and video from this presentation are not currently available. The research is ongoing. Once the results are published they will be posted.]
Non-ionic surfactants are an important component of industrial metalworking cleaners. For beverage can manufacturing these cleaners need to deliver good cleaning performance with low foaming properties. These attributes can be strongly influenced by different types of non-ionic surfactants.
With the help of Design-Expert® software an experimental design was set up to find an optimal balance between three selected surfactants. The experimental formulations were tested for foaming properties and cleaning performance. All experimental data was analyzed by the software to calculate models with good fit.
Stratasys’ PolyJet technology jets a thin layer of resin materials onto the surface, which is then polymerized on the surface using UV light. The process is then repeated multiple times, each layer adding thickness to the model.
When jetting ink for 3D printing, a support material must be also printed beneath model material overhangs. Due to the printing sequence and the internal environment inside the printer, the support material must have a certain resilience and thermal stability, and provide adequate surface quality of the model post support removal.
The primary topic of this experiment was to add thermal stability to the support material, as the existing formulation would melt as ambient temperature increased during the printing process. Since runs are expensive, a goal is to use a minimal number of trials.
The control of ingredients quantities in pharmaceutical formulations is critical to a drug product quality. Often, the Active Pharmaceutical Ingredient (API), and sometimes specific excipients, concentrations testing in the final product is required to release a batch.
By applying Design of Experiments (DoE) and Multi-Variate Data Analysis (MVDA), a UV-Vis spectroscopy chemometric model has been developed which is capable of measuring simultaneously both ingredients contents in the formulation. The test can easily be applied to finished or bulk product and the results are immediately available.
Design of experiments is typically presented as a “one shot” approach. However, it may be more efficient to divide the experiment into smaller pieces, thus expending resources in a smarter, more adaptive manner. This sequential approach becomes especially suitable when experimenters begin with very little information about the process, for example, when scaling up a new product. It allows for better definition of the design space, adaption to unexpected results, estimation of variability, reduction in waste, and validation of the results.
A nice new addition to Design Expert is the KCV designs (Kowalski, Cornell, and Vining 2000 and 2002) for experiments that involve both mixture components and process variables. This talk presents an overview on these designs. It begins with a brief history of their origin. It then motivates the basic approach for the construction of these designs and contrasts this approach to other approaches popular at that time. It then discusses some of the subtleties involved in analyzing these designs. An example illustrates their use.
In today’s Industry 4.0, industrial processes are becoming increasingly complex, presenting significant challenges to the industrial experimenter. In particular, modern experimental design practice can often lead to non-standard situations. In this talk I will discuss some examples of the non-standard experimental design situations I’ve encountered in modern practice, with the common denominator in all these situations being a split-plot treatment structure.
In this presentation, I will speak from the heart on my lifelong involvement with design of experiments. Starting with my early years as a new engineer using DOE, I will then focus on providing DOE for 35 years at Stat-Ease. In wrapping up, I will give a sneak peek at version 13 of Design-Expert® software.
By way of example, this presentation lays out a strategy for design of experiments (DOE) that provides maximum efficiency and effectiveness for development of a robust process. It provides a sure path for converging on the ‘sweet spot’—the most desirable combination of process parameters and product attributes. Whether you are new or experienced at doing DOE, this talk is for you (and your organization's bottom line!).
Pat Whitcomb details the cost-saving mixture-process models developed by Scott Kowalski, John Cornell and Geoff Vining (KCV). Design-Expert® software, version 12, drops this modeling tool right into the user's hands. See how it reduces the number of model terms and thereby reduces the number of runs required to estimate the complex relationship between mixture and process variables. Estimated length: 45 min.
Mark Anderson, Stat-Ease Consultant and lead author of the DOE/RSM/Formulation Simplified trilogy, will detail what design of experiments (DOE) can do for you when catalyzed with DX12’s world-class statistical tools.
Martin Bezener, Stat-Ease Consultant, introduces Design-Expert v12’s new tools for logistic regression.
Learn about the highlights for innovative new statistical tools and graphical presentations in version 12 of Design-Expert software. Enjoy an enlightening demonstration of DX12’s fabulous new capabilities presented at a beginner’s level.
Discover how to optimize your process while avoiding impossible factor combinations.
Learn about factorial design, the core tool for DOE, followed by a peek at response surface methods (RSM).
This case-study driven webinar is a must for all who experiment on APIs. Learn how to apply statistically valid, multifactor and multicomponent testing strategies that catalyze your development work.
Discover new features in Design-Expert® software, version 11, with a demonstration of the overhauled interface.
Use mixture design tools for multi-component product development and optimization, perfect for formulators. Learn why factorial designs won't work.
Pat Whitcomb reveals some tricks for making the most of your DOE.
Use graphical tools (half-normal & Pareto plots) to select effects quickly and accurately.
How to use automatic model selection tools to build on appropriate models. Pros and cons of the methods are discussed.
Learn how multivariate analysis (MVA) methods can be used in combination with design of experiments (DOE) tools. Presenters demonstrate how to build an optimal design from principal components and use the DOE results to find an optimal compound.
Learn how to use power and precision to properly size factorial and RSM designs.
Discover how split-plot designs are the best choice when you have hard-to-change (HTC) factors.
Review strategies for running confirmation to verify the results of an experiment.
Topics include foldovers, semifoldovers, building a CCD from a one-factor-at-a-time (OFAT) study, and optimal augmentation for RSM designs.
We have an answer for your question: "How many runs do I really need?"
A briefing on QbD, along with state-of-the-art response surface methods (RSMs) for developing a robust design space.
Discover how propagation of error and tolerance analysis can account for variation in your system.
Topics include using optimal designs for constrained design spaces, adding categoric factors, higher-order models, and design augmentation.
Review central composite designs and multiple response optimization.
Learn why, when, and how to use algorithmic (optimal) designs for your experiments.
An introduction to factorial design. What is DOE and how can you use it?
Historical data can be difficult to analyze - look out for these common issues.
Verification is an essential final step to improving a product or process. Learn how DOE makes this a breeze.
A dual response approach to response surface methods (RSM) provides a statistically sound way to make your process more robust.
Learn the differences between replicates, duplicates, and repeats, as well as the reasons for using each. Cost-based decision selection of one versus another are discussed.
Learn how Design-Expert's optimization tools help you find the "sweet spot" for your product or process.