Advanced Design of Experiments Training Course

Expand All

Many R&D experimenters, design and development engineers and scientists are using an OFAT (one-factor-at-a-time) approach to their experimental designs. In addition to the issue of inefficiency (unnecessarily large number of experiments), 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.

Collapse

The course is presented in two modules:
Module 1 – Three-day programme in which the participants will be trained in the use of DOE factorial techniques, including the statistics that underlie DOE. The objective of factorial DOE is to identify the few factors, among many possible factors, which have an effect on the response. On day three of module 1, delegates will partake in a practical workshop designing experiments, applicable to their own area of work.

Module 2- Two-day programme devoted to the optimization techniques of Response Surface Methodology (RSM) and Mixture Design. On day two of module 2, delegates will partake in a practical workshop designing experiments, applicable to their own area of work.

The training course is built around more than 30 real-life case studies, mainly from the chemical, electronics, and engineering manufacturing industries. The cases studies have been carefully selected to demonstrate all of the key principles of DOE. Delegates will work their way through these practical case studies learning the techniques of DOE and the advanced software that is used to design and analyze experiments.

Module 1
Days 1 – 3: Factorial and Fractional Factorial Designs

Day 1: Statistics that Underlie Design of Experiments

Introduction to basic statistics-understanding variation in processes
Mean, standard deviation, degrees of freedom
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
Hypothesis testing – 2-sample t-test and F-test
Analysis of variance (ANOVA) and introduction to experimental design with one factor

Note: Regression analysis will be left over to Day 1 of module 2.

Days 2 – 3: Design and Analysis of Experiments

Planning the experiment and determining the experimental objective.
Explanation of the terminology – responses, factors, levels, replication, repetition, randomization, design points, design runs
Understanding the statistical importance of avoiding excess variation in experiments – the role of measurement and careful control of the experiments
Establishing the basic principles with a two factor and three factor design – explanation of main effects and interactions
Analysis of experimental results using the two-sample t-test, ANOVA, and the probability plot
Screening out the non-significant factors
Understanding how to interpret interaction plots
The role of blocking in DOE
The need to reduce the number of runs when there are a large number of factors involved – the concept of using fractional factorial designs
“Folding over” to improve resolution of factorial designs
Practical exercise designing an experiment applicable to the delegates own area of work.

Module 2

Days 4 and 5: Optimization with Response Surface Methodology (RSM) and Mixture Designs

Overview of the factorial designs linking the work covered in Module 1 to the RSM techniques in Module 2
Regression analysis – modelling with regression - lack-of-fit analysis, correlation analysis, R-squared, R-squared adjusted, R squared predicted
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
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 – Optimal mixture designs
Combined Designs using combination of mixture components and process factors
Practical exercise designing an experiment applicable to the delegates own area of work.

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

Note: Course can be presented in four days if Mixture Design is not required.

All R&D personnel
Design and development engineers and scientists
Process Engineers

A prior knowledge of statistics is not required. However, the participants should have knowledge of mathematical principles, for example, Leaving Certificate or A-level mathematics.

Participants achieve the following learning outcomes from the programme;

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 response surfaces
Select factor and component levels to simultaneously optimise multiple responses

Albert Plant

With a background of thirty year’s practical experience, Albe...

Read More
Grainne Heneghan

Grainne is a highly experienced Continuous Improvement and Lean...

Read More

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.

Minitab is a leading brand of general-purpose statistical software with powerful DOE capability. Design Expert is special-purpose DOE software incorporating the most advanced mathematical techniques available for DOE, and utilizing state of the art graphics.

Delegates will need to have either Minitab (versions 20. 21 or 22) or Design Expert loaded on laptop computers. Trial versions of the software, suitable for training, are available - Design Expert (free 45 day trial available on www.statease.com) or Minitab 20 or 21 (free 30 day trial available on www.minitab.com).

Mixture Design is presented only with Design Expert software.

Collapse All

Download brochure

Snapshot

Course Code

CPI002

Duration

5 training days

Delivery Mode

This programme is delivered by or In-Company training

stdClass Object
(
    [primary_category] => 
    [primary_slug] => 
    [categories_slugs] => Array
        (
            [0] => continual-process-improvement-statistical-applications-minitab
        )

    [parent_categories_slugs] => Array
        (
            [0] => lean-six-sigma-process-project-management-lean-six-sigma
        )

)

News & Updates

Don’t confuse Mixture DOE with Factorial DOE and RSM

When undertaking formulation work don’t confuse Mixture DOE’s with Factorial/RSM DOE’s A potential error among DOE practitioners undertaking formulation w...

Reduce the cost of your DOE’s while still obtaining satisfactory results

A Remarkable Resolution V DOE with six factors in 22 runs It is always desirable to have fractional factorial DOE’s at Resolution V upwards. Main effects are ...