Design of Experiments - 230414 Flashcards

1
Q

What is design of Experiments?

A

Influence of multiple inputs (factors) in outputs (responses), experiment with all the factors at the same time.
Goal: More results, less experiments.
Agriculture: what factors are important to get an outcome. Medicine, Pharmaceutical companies: which of the factors influence the other ones?
Factors: temp, humidity. Response: thickness of coating.

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2
Q

Non optimal examples

A

Trial and error, not very good method:
* Temperature, time -> Yield.
* Adjust one of them -> Yield. Adjust another one -> Yield.
OFAT (One Factor at a Time):
* More efficient, but could still fail. Not exploring the whole behavior of the system in the design space.

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3
Q

Full factorial DOE

A

Full factorial DOE: Test the extremal points (+1 max and -1 min values) and its combinations (2^k).
Discover trends, not necessarily the optimal solution, less tests.
Do it twice to find if the order of the experiments does not generate any unexpected interaction and evaluate statistical scattering.

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4
Q

Why DOE?

A

Shorter testing, cost effective, statistical tolerancing.

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5
Q

General process

A

Expert knowledge (or random numbers and evaluate after that), select factors and response, design space, choose DOE design,
Run experiments, Find optimal settings, Test runs with optimal parameter, Modify process

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6
Q

Two general conditions

A
  1. Measurability: pressure, temperature, material types, fyber content.
  2. Adjustability:
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7
Q

Statistics’ uses in DOE

A

Factors: construction of design spaces.
Responses: Real effects vs. Random results, prediction of process results (approximation function)

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8
Q

Axioms of Probability

A
  1. Each probability goes from 0 to 1.
  2. Safe event: 1
  3. Probability of two events that are mutually exclusive: 0
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9
Q

How does the density function relates to the cumulability frequency?

A
  1. Failure probability is the integral of the density function.
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10
Q

Number of fringes

A

square root of the number of elements

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11
Q

Why is the Weibull distribution so used?

A

Same distribution, variation in Weibull parameters result in different shapes (exponential and normal)

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12
Q

t test

A

Hypothesis tests. Only for normally distributed data.
Null hypothesis: mean values are identical. Otherwise: there is an actual effect on the factor.

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