ME 388 – Applied
Instrumentation Lab
Spring 2012
Dave Bayless, PhD, PE, FASME
Loehr Professor of Mechanical Engineering
248 Stocker Center
e-mail: bayless@ohio.edu
Office Hours: M,T,W,Th 15:30 – 16:30
Text
• ME 388 Laboratory Manual can be found at
http://www.ohio.edu/people/bayless/seniorlab
• Experimental Methods for Engineers (Holman)
may be useful, but is not required
Grading
Subject
Unit Weight
Total
Five Experimental Lab Exercises
@ 10%
50%
Mastery level formal report
@ 25%
25%
CIM laboratory Project
@ 5%
5%
DOE Project
@ 5%
5%
Formal Lab Introductory material
@ 5%
5%
Final Exam
@ 10%
10%
Total
100%
Purpose
• Enhance fundamental engineering learning
with lab experiments
• Gain experience and improve experimental
techniques
• Improve data reduction and analysis skills
• Improve written communication skills
Outcomes
• Mastery
• Competence
• Awareness
Course Mastery Outcomes
• Ability to perform curve-fitting of multivariate
data sets
• Ability to calculate the error/uncertainty
propagation for calculations that include multiple
terms with uncertainties.
• Writing and editing clear and effective laboratory
reports, including the creation of “professional
quality” graphics for figures, tables, plots and
charts.
Course Competence Outcomes
• Ability to use common measurement equipment
• Ability to apply previously-learned engineering
concepts to compare theoretical predictions with
actual experimental results in diverse, practical
mechanical engineering experiments.
• Ability to program and use CNC machines to
manufacture simple parts
• An ability to interpret tensile test data
Course Awareness Outcome
• Awareness of Design of Experiments (DOE)
statistical techniques
• DOE Exercise will give you a chance to
interpret a test matrix
Spelling and Grammar
•
•
•
•
•
Write in the 3rd person
Use spelling and grammar checker in Word
Adopt the style of a textbook or journal article
See formal report guidelines in lab notes
“Write smart”
– “Outlying data were rejected.” instead of “Bad
data was thrown out.”
– Edit your work to be concise!!
Mullen Burst Strength (psi)
Figure Example
600
500
400
300
200
Teflon - Reactive 275
Ryton B - Reactive 430
Ryton-Sulfuric 605
Omnisil-Sulfuric 550
100
0
0
100
200
300
400
500
Date of Sampling
Figure 1. Burst strength as a function of time
Table Example
Table 4. Dependent process variables as a function
of the DOE number.
Dependent variables
DOE
No.
Ram pressure
(MPa)
Specific pressure
(MPa)
1
14.71
459.2
2
15.09
471.1
3
14.30
446.6
4
13.69
427.3
5
14.77
461.1
6
14.37
448.3
7
13.30
415.0
Use computer generated schematics

tv
p
i
p
1

i
3
tw
Figure 5: Schematic of stressed multi-void tube due to pressure
Equations
• Use MS Word Equation editor
• Number equations sequentially, right justified
• See Lab notes
Statistical Analysis Review
• Mean
• Standard Deviation
• Sample Size
Mean
n
x1  x2  x3  ...  xn 1
x
  xi
n
n 1
Standard Deviation
xi  x
Simple variance
Sample variance
2
2
2
2
x  x   x2  x   x3  x   ...  xi  x  
2  1
n 1


1 n
2
x

x

i
n 1 1
Standard deviation of a sample

x1  x   x2  x   x3  x 
2
2
2
n 1

 ...  xi  x

2
 1 n

xi  x

 n  1 1

1
2 2


Histogram and normal distribution
Standard deviation and data
How many samples are enough?
n
1
2
3
4
5
6
7
xi
90
89
91
87
80
90
92
xave
--89.50
90.00
89.25
87.40
87.83
88.43

--0.707
1.000
1.708
4.393
4.070
4.036
Can “outlying” data be ignored?
• Determine if there is a physical basis for the
suspect data (i.e., the TC broke, etc.)
• Chauvenet’s criteria for data rejection
Chauvenet’s criteria
1. Calculate xave and  for data set
2. Get dmax/ for the specific sample size from a
table
3. Calculate dmax = (dmax/) × 
4. Determine if the most “reject-able” data is
larger than this value, d = |xave – xi|
5. Reject outlying data and then recalculate xave
and  for data set
Chauvenet’s Example
Chauvenet’s Example
4
2
3
17
5
(5 data points, n=5)
Average = 6.2
Standard Deviation = 6.14
Which one to reject?
 Technically, examine at them all
 Realistically, focus on “17”
d max
 1.65
For n = 5, reject at

d
17  6.2
6.14
 1.76 ← REJECT 17
How sure are you of your data?
• All measurement instruments have a degree of
uncertainty when taking a reading
• Uncertainty values for a particular instrument
is usually given or can be determined
• For calculated parameters, the uncertainty is a
function of the uncertainties of the measured
parameters.
Uncertainty calculation
Let x  f (v, y, z )
1
22
2
2

x  
x  
x 

Wx  Wv   W y   Wz  
v  
y  
z  



• Report uncertainty as a % of calculated value
Wx
Uncerta int y 
 100%
X
Regression Analysis
• Pertains to reporting of a “least-square” or
other type of curve fit to your data
• You must report the equation and the
correlation coefficient (R) or the coefficient of
determination (R2)
• R-values should be presented with the equation
and a graph of the data
Formal Report
•
•
•
•
•
•
Abstract
Introduction
Experimental Apparatus
Results and Discussion
Conclusions and Recommendations
Appendices
– Uncertainty analysis
– Data
Abstract (250 words)
•
•
•
•
•
Purpose
What was done?
Significant parameters measured or set
Measurement results (summarized)
Quantitative comparisons (i.e., to published
values)
Introduction
•
•
•
•
Provide background information
Establish significance of work
Introduce work and motivation for experiment
Introduce equations that are pertinent to data
analysis and purpose of the lab
Experimental Apparatus
• Describe experimental equipment
configuration using schematic diagrams
• Explain test procedure
• Present any calibration data
• Show (tabulate) all uncertainty values
measurements that were taken
Results and Discussion
• Present results (analyzed data) in graphical
form
• Discuss results and sources of error (explain
why the data did what it did)
• Develop logical and reasonable explanations
with regard to data behavior
• Make quantitative comparisons
• Discuss uncertainty and if it accounts for any
known or obvious discrepancies
Conclusions and Recommendations
• Summarize results quantitatively
• Summarize any comparisons
• Start with results of most importance or
significance
• Address all significant points
• Make sound recommendations
– Things that could be improved
– Additional work that could be done