design of experiments with minitab

Design of Experiments (DOE) Basic

Course Description
Design of Experiments is an off-line quality improvement technique that can be employed to dramatically improve industrial products and processes, shorten development time, and provide a structured method for improved decision making. Through its use, it is possible to isolate the cause and effect linkages between product/process variables and the resulting output measures of function, quality, cost, and performance. 

This course provides participants with an in-depth understanding of the basic principles of experimental designs for screening and characterizing processes, including planning a DOE, simple comparative experiments, main effects, interactions, and detection of curvature. Experimental design includes one-factor optimization, general full factorials, two level factorials, and fractional factorials.  

Actual industrial examples are emphasized along with a unique group learning technique of solving problems incorporating simulated noise allowing the full thinking process to be internalized. By solving these problems within the resource constraints given, participants learn to apply DOE economically, identify factors, set factor levels, define experimental goals, select the appropriate experimental design, and develop analysis skills for interpretation and action planning.

Who Should Attend
This course is designed for engineers, quality professionals, researchers, and managers who need to understand and use DOE for process screening and characterizing.

Learning Objectives
Through training, participants will:

  • Gain knowledge of DOE fundamentals including randomization, replication, and confounding
  • Be able to optimize one continuous factor
  • Carry out a simple comparative experiment
  • Be able to identify key factors through screening designs
  • Know how to assess main effects, interactions and detect curvature
  • Know effective strategies for process improvement and why DOE methods are an improvement on other strategies such as One-Factor-at-a-Time Experimentation

Course Outline
How Experimental Design is Useful in Industry and Statistical Fundamentals

  • The Need for Experimental Design in Industry
  • Using Experimental Design as part of a Total Quality Solution
  • Attributes of a Well-Designed Experiment
  • Hypothesis Testing and Confidence Intervals

Single-Factor Experiments for Comparisons

  • t-tests
  • Analysis of Variance for Simple Comparative Experiments
  • Simultaneous Multiple Comparisons
  • One Factor Optimization

Factorial Experiments For Characterization

  • The General Factorial Design
  • The 2k Factorial Design
  • Main Effects and Interactions: Analysis and Interpretation
  • Blocking in Factorials
  • Center Points
  • Unreplicated Full Factorial Designs
  • Group Exercise 1:  Live experimentation via simulation with resource constraints

2k-p Fractional Factorials For Screening

  • Hierarchy of Terms and Alias Structure
  • Resolution III, IV and V designs
  • Foldover Designs
  • Sequential Experimentation
  • Group Exercise 2:  Live experimentation via simulation with resource constraints

Experimental Planning

  • DOE Planning Template

Prerequisites
Basic SPC or the equivalent

Course Format
24 hours
Instructor-led class training, with opportunities to practice learned skills using prepared data, live demonstrations, and data collected real time in class
Minitab or JMP Statistical Software

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