Design of Experiments for R&D

Design of Experiments for R&D

Six Day Training Course

Background

Many R&D experimenters are using an OFAT (one-factor-at-a-time) approach to their experimental designs. In addition to the issue of inefficiency, this approach fails to identify often crucially important interaction effects among factors. There are available to experimenters advanced analytical tools based on advanced mathematical techniques and utilising special computer software, which will enable them to gain a deep understanding of their processes, including the impact of interactions among factors, and to do so in the most efficient manner with minimum numbers of experimental runs. These modern DOE tools will be presented on this training course.

Learning Outcomes

On successful completion of this training course, delegates should be able to:

• Plan and execute screening experiments to select factors that affect the process
• Analyse factor effects and interaction effects using specialist computer software
• Use advanced mathematical techniques to construct and model process and mixture response surfaces
• Select factor and component levels to simultaneously optimise multiple responses

Who Should Attend

• All R&D personnel
• Design and Manufacturing Engineers
  
  A prior knowledge of statistics is not required. However, the participants should have knowledge of mathematical principles, for example, Leaving Certificate mathematics.

Course Programme

Summary

The course is presented in two modules –

• Module 1 – Three-day programme in which the participants will be trained in the use of DOE screening techniques. The objective of screening is to identify the few factors, among many possible factors, which have an effect on the response.
• Module 2- Three-day programme devoted to the optimization techniques of Response Surface Methodology (RSM) and Mixture designs. On day three of module 2, delegates will partake in a practical workshop designing experiments, applicable to their own area of work.

Module 1

Days 1 – 3: Screening with Factorial and Fractional Factorial Designs

Day 1: Statistics that Underlie Design of Experiments
Introduction to basic statistics-understanding variation in processes

• Mean, standard deviation, COV, degrees of freedom
• Constructing and understanding the histogram
• The normal and standard normal distributions – their importance in DOE
• The normal probability plot and the Anderson Darling statistic – understanding the importance of normality and how to test for normality
• Explanation of tail values, alpha values and p-values
  • Hypo thesis testing – 2-sample t-test and F-test
• Analysis of variance (ANOVA) and introduction to experimental design with one factor
• Simple linear regression
• Understanding the importance of analyzing residuals – calculating and plotting residuals

Days 2-3: Design and Analysis of Experiments

Module 2

Days 4 – 6: Optimization with Response Surface Methodology (RSM) and Mixture Designs

• Overview of the factor screening designs linking the work covered in Module 1 to the RSM techniques in Module 2
• Review of regression principles and more advanced regression material including multiple regression and lack-of-fit analysis, correlation analysis, R-squared, and R-squared adjusted
• The objectives of RSM - Optimizing the settings of the input factors which affect the response
• Understanding the quadratic model – selecting the appropriate model – adjusting the model for best results
• Finding the best compromise between multiple responses using advanced mathematical techniques and computer software
• D-optimal designs – using advanced mathematical techniques and computer software to select the most appropriate runs in a reduced set of candidate points
• Mixture designs – experimenting with component proportions to achieve optimum formulation
• Designs with constraints – D-optimal mixture designs
• Combined Designs using combination of mixture components and process factors

Approximately a half day will be devoted on Day 3 of Module 2 to a practical workshop designing experiments applicable to the delegates’ own work.

The time span between the presentations of the two modules can be arranged to suit the requirements of the delegates.

(The course can also be presented as a 5-day course if Mixture Experiments are not applicable)

Software

Design Expert is special-purpose DOE software incorporating the most advanced mathematical techniques available for DOE, and utilizing state of the art graphics. Minitab is a leading brand of general-purpose statistical software with powerful DOE capability. The tutor is highly experienced in the use of both types of software, and he will train the course participants in the use of their chosen brand to carry out the design and analysis of their experiments.

Delegates will need to have either Design Expert (versions 7 or 8) or Minitab (versions 15 or 16) loaded on laptop computers. Trial versions of the software, suitable for training, are available - Design Expert 8 (free 45 day trial available on www.statease.com) or Minitab 16 (free 30 day trial available on www.minitab.co.uk)

Course Tutor

Albert Plant

Testimonials

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Course Times

9.00am - 5.00pm

In-House Courses

For In-House courses the tutor will contact you in advance to discuss the course programme in more detail in order to tailor it specifically for your organisation. Where appropriate and facilitated by the company, the course can be run using Minitab or Design Expert software.

Course Manual

Delegates will receive a very comprehensive course manual, which explains the underlying statistics, describes the principles of experimental design, explains in detail how experiments are designed and analysed, includes examples of several practical case studies, includes instructions for operating the software, and incorporates completed versions of all the course exercises and graphs, including the output from Design Expert or Minitab computer software. The course manual will provide a very useful reference for participants undertaking the design and analysis of experiments when they return to their workplace.