Skip to content

MANE 6313

Week 1, Module F

Student Learning Outcome

Analyze simple comparative experiments and experiments with a single factor.

Module Learning Outcome

Explain the fundamental concepts found in chapter 1 of the textbook.

Some Typical Applications

  1. Improved process yields

  2. Reduced variability and closer conformance to nominal or target requirements

  3. Reduced development time

  4. Reduced overall costs

Engineering Design Activities

  1. Evaluation and comparison of basic design configurations

  2. Evaluation of material alternatives

  3. Selection of design parameters so that the product will work under a wide variety of field conditions (robust design)

  4. Determination of key product design parameters that impact product performance

Basic Principles

  • Replication. Repetition of basic experiment. Two important properties.

    • Allows an estimate of the experimental error to be obtained

    • Replication permits a more precise estimate of \(\bar{x}\)

  • Randomization. The allocation of the experimental material and the order of the experiments. Statistical methods require that the errors are independent. Randomization usually makes this assumption valid.

  • Blocking. A technique used to improve the precision of an experiment. A block is a portion of the experimental material that should be more homogeneous than the entire set of material.

Guidelines for Designing Experiments

  1. Recognition of and statement of the problem

  2. Choice of factors, levels and ranges

  3. Selection of the response variable

  4. Choice of the experimental design

  5. Performing the experiment

  6. Statistical analysis of the data

  7. Conclusions and recommendations

History of DOE

DOE can be divided into four eras

  1. Agricultural era

Pioneered by Fisher in the 1920's and 1930's (3 principles)

  1. Industrial era

Development of RSM methods by Box and Wilson (1951).

  1. Western industries and Taguchi

increasing interest in quality improvement and impact of Taguchi (late 1970's - early 90's)

  1. Modern era

renewed general interest in statistics, use in many new fields, focus on industrial problems

From another point of view

Checklist for Designed Experiments

From another point of view, continued

Checklist for Designed Experiments

Using Statistical Techniques in Experimentation

  • Use your non-statistical knowledge of the problem

  • Keep the design and analysis as simple as possible

  • Recognize the difference between practical and statistical difference

  • Experiments are usually iterative

Case Study

The wrong approach - one factor at a time

One Factor at a time Strategy

Case Study, continued

Interactions are an important concept

Interaction Between Two Variables

Case Study, continued

Factor Effects

Case Study, continued

Three Factor Experiment

Case Study, continued

Four Factor Experiment