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Design of Experiments (DOE) Training for Engineers

The Design of Experiments Training, DOE Training for engineers course is designed to show you both theory and hands-on prerequisites important to run and execute the DOE.

DOE or Design of Experiments is now and then called a Statistically Designed Experiment. DOE is a thought to be a deliberately arranged and executed investigation to give point by point data about the impact on a reaction variable because of at least one components: One– Factor– at– a– Time (or OFAT). 




#Audience

DOE Training is a 2-day course designed for:

  • Quality managers and engineers 
  • SPC coordinators 
  • Quality control technicians 
  • Consultants 
  • R&D managers, scientists, engineers, and technicians 
  • Product and process engineers 
  • Design engineers 

#Training Objectives

  • Develop and apply necessary skills required later to solve, design, or optimize more complex problems or multiphase systems 
  • Perform a full DOE test matrix, in both randomized and blocked way 
  • Build a model 
  • Run a DOE to solve problems and to optimize a system 
  • Analyze and interpret the DOE results, using ANOVA or graphical methods whichever is relevant 
  • Understand and perform analysis for experiments: main and interactive effects, experimental error, normal probability plots, identification of “active” efforts, and residual analysis. 
  • Recognize what parameters have the most impact on the quality of a product or the productivity of a process 
  • Set up a partial factorial DOE by applying confounding principal 
  • Analyze and interpret the results from the partial factorial DOE 
  • Understand the fundamentals and advantages of Robust DOE 
  • Decide when a Response Surface DOE needs to be executed 
  • Pick the relevant Response Surface Design 
  • Analyze and interpret the results of Response Surface 

#Course Outline

  • What is DOE? 
  • Elements of an experiment 
  • Elements of the scientific methodology 
  • How to incorporate the scientific method into an experiment 
  • How much data is enough for an experiment? 
  • Determine various features of the design of experiments method 
  • Experimental geometry 
  • Response mapping 
  • Relationship between the principals of a DOE with the definitions associated with it 
  • What are the advantages of using DOE compared to conventional experimentation methods 
  • Steps to design an experiments 
  • Full Factorial Experiments using Cube Plots 
  • Minitab introduction 


Design of Experiments Training 
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