* Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and conduct tests
* Case studies place DOE techniques in the context of different industry sectors
Table of Contents
INTRODUCTION TO INDUSTRIAL EXPERIMENTATION
Some fundamental and practical issues in industrial experimentation.
FUNDAMENTALS OF DESIGN OF EXPERIMENTS:
Basic principles of Design of Experiments:
Degrees of freedom.
Metrology considerations for industrial designed experiments:
Measurement system capability.
Some tips for the development of a measurement system.
Selection of quality characteristics for industrial experiments.
UNDERSTANDING KEY INTERACTIONS IN PROCESSES:
Alternative method for calculating the two order interaction effect.
Synergistic interaction vs antagonistic interaction.
A SYSTEMATIC METHODOLOGY FOR DESIGN OF EXPERIMENTS:
Barriers in the successful application of DOE.
A practical methodology for DOE:
Analytical tools of DOE:
Main effects plot.
Pareto plot of factor effects.
Normal Probability Plot of factor effects.
Normal Probability Plot of residuals.
Response surface plots and regression models.
Model building for predicting response function.
Confidence interval for the mean response.
Geometric and non-geometric P-B designs.
FULL FACTORIAL DESIGNS:
Example of a 2 squared full factorial design:
Objective 1: Determination of main/interaction effects which influence mean plating thickness.
Objective 2: Determination of main/interaction effects which influence variability in plating thickness.
Objective 4: How to achieve a target plating thickness of 120 units?
Example of a 2 to the power of 3 full factorial design:
Objective 1: To identify the significant main/interaction effects which affect the process yield.
Objective 2: To identify the significant main/interaction effects which affect the variability in process yield.
Objective 3: What is the optimal process condition?
Example of a 2 to the power of 4 full factorial design:
Objective 1: Which of the main/interaction effects affect mean crack length?
Objective 2: Which of the main/interaction effects affect variability in crack length?
Objective 3: What is the optimal process condition to minimize mean crack length?
FRACTIONAL FACTORIAL DESIGNS:
Construction of half-fractional factorial designs.
Example of a 2 to the power of (7-4) factorial design.
An application of 2-level fractional factorial design.
Example of a 2 to the power of (5-1) factorial design:
Objective 1: To identify the factors which influence the mean free height.
Objective 2: To identify the factors which affect variability in the free height of leaf springs.
How do we select the optimal factor settings to minimize variability in free height?
SOME USEFUL AND PRACTICAL TIPS FOR MAKING YOUR EXPERIMENTS SUCCESSFUL:
Get a clear understanding of a problem.
Conduct exhaustive and detailed brainstorming session.
Teamwork and selection of a team foe experimentation.
Select the continuous measurable quality characteristics or responses for the experiment.
Choice of an appropriate Experimental Design.
Randomize the experimental trial order.
Replicate to dampen the effect of noise or uncontrolled variation.
Improve the efficiency of experimentation using blocking strategy.
Understanding the confounding pattern of factor effects.
Perform confirmatory runs/experiments.
Optimization of a radiographic quality welding of cast iron.
Reducing process variability using Experimental Design technique objective of the experiment.
Slashing scrap rate using fractional experiments.
Optimizing the time of flight of a paper helicopter.
Optimizing a wire bonding process using Design of Experiments.
Training for Design of Experiments using a catapult.
Optimization of core tube life using designed experiments.
Optimization of a spot welding process using Design of Experiments.
- No. of pages: 176
- Language: English
- Copyright: © Elsevier 2003
- Published: September 5, 2003
- Imprint: Elsevier
- eBook ISBN: 9780080469959
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