Stat-Ease offers free educational webinars to those interested in design of experiments (DOE) and Design-Expert® software. Topics range from beginner to advanced levels. Material may be new or drawn from our ever-popular DOE workshops.
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"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
Response surface methods (RSM) provide a quick path to the peak of process performance. This webinar presents an array of RSM designs to choose from – central composite, Box-Behnken and optimal (custom). Learn when each design excels. Also find out how to handle categoric factors, discrete numeric levels and complex constraints involving multiple factors. Discover how to set up the right RSM design for your unique experimental needs.
Can’t make this time? Register anyway so that you are notified when the recording is ready.
Date: Wednesday, January 20, 2021
Time: 10:00am Central US Time
Advance your R&D experimentation skills via this essential webinar on mixture experiments. A compelling demo lays out what makes mixture design of experiments (DOE) so effective for accelerating your formulation efforts. Discover how to:
The fuel provided in this 1-hour webinar will kick-start your first designed experiment.
In this advanced-level webinar, Stat-Ease Consultant Pat Whitcomb discusses robust design, propagation of error, and tolerance analysis. Propagation of error (POE) accounts for variation transmitted from deviations in factor levels. It finds the flats—high plateaus or broad valleys of response, whichever direction one wants to go—maximum or minimum; respectively. Tolerance analysis drills down to the variation of individual units, thus facilitating improvement of process capability.
This talk deals with thorny issues that confront every experimenter: How to handle results that fit badly with your chosen model. Design-Expert software provides graphical tools that make it easy to diagnose what is wrong—damaging outliers and/or a need for transformation. A variety of case studies will demonstrate the value of these diagnostics. They make save you a great deal of embarrassment for incorrect interpretation of experimental results, or the opportunity lost by letting bad data obscure a breakthrough discovery.
Step up your design of experiments (DOE) know-how via this essential briefing on this multifactor-testing tool. A quick demo lays out what makes statistical DOE so effective for accelerating R&D. Discover how DOE will find your vital few factors and reveal breakthrough interactions.
Discover the secrets to customizing your experiments using optimal (custom) designs. Learn the importance of adding lack of fit points and replicates. All these issues are considered at a practical level – keeping the actual experimenters in mind.
Discover 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.
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.
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.
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. 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 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.
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.
Use mixture design tools for multi-component product development and optimization, perfect for formulators. Learn why factorial designs won't work.
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.
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.
Review central composite designs and multiple response optimization.
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.