Introduction to Design of Experiments

Introduction to DOE with AI Agents

Teams based training (three 2 hour sessions)

Design of Experiments is a great tool to help you cut design and development times while assuring that products and processes are effectively and efficiently characterized. However, classical DOE takes a great deal of time to learn and relearn. Software can be daunting to new users or users who don’t conduct DOE’s every week.

In these virtual sessions, we free you from the use of commercial software packages and learning the rigors of statistics and complex software interfaces. Our AI Agent automates the entire process from selection of factors/levels and responses to selection of the design matrix, analysis of the data, and generation of a report written in layman’s terms. This class is for the engineer or scientist who is fascinated by their technology but not the rigors of the classical design of experiments, yet needs the power of DOE!

  • Scientist, engineers, managers, technicians. Anyone who wants to quickly attain an applications based understanding of Design of Experiments.

    DOE sessions will enable engineers and scientists to begin real DOE’s on their processes and technologies. Design of Experiments is the premier tool for characterization of products and processes. Blended with AI Agents, it revolutionizes the application of experimental design.

  • These sessions are on-line but can be taught in-house as well.

  • In person sessions can be customized to fit your schedule. We can offer you three 2 hour sessions.

    The online option will be taught 2 hours per day (Monday - Thursday) for three sessions.

  • DOE I  

    Course topics to be covered include 

    Introductions, Goals, Expectations 

    The Need for Experimental Design in Industry 

    How AI changes the whole DOE equation 

    Examples of Applications  

    What is an Experimental Design? 

    What are the Benefits? 

    Why Conduct an Orthogonal Array? 

    Basic concepts: Effects, Interactions, Orthogonal arrays 

    Graphical Analysis using examples and AI Agent​

    Pareto Charts​

    Main Effects​

    Interaction Plots​

    Contour Plots​

    RSM Plots 

    Screening Approaches vs. Modeling Approaches 

    Checklist of Detailed Planning Questions 

    Why Confirmation Trials are Important 

    Common Pitfalls for Those New to Design of Experiments 

    Full-Factorials and Fractional-Factorials 

    Computer optimal designs, introduction

Enrollment Details

Format: Live Online/Virtual

Submit your interest form (LEFT) with the class date. We will contact you with additional enrollment details.

Cost: $300.00 per participant.

Includes:

  • Detailed Participants Guide

  • eBook Engineering Today’s Designed Experiments

Available Sessions:

  • Contact us for available dates