ME 388 – Applied Instrumentation Lab Spring 2012

1. ME 388 – Applied Instrumentation LabSpring 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

2. 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

3. Grading

4. Purpose • Enhance fundamental engineering learning with lab experiments • Gain experience and improve experimental techniques • Improve data reduction and analysis skills • Improve written communication skills

5. Outcomes • Mastery • Competence • Awareness

6. 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.

7. 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

8. Course Awareness Outcome • Awareness of Design of Experiments (DOE) statistical techniques • DOE Exercise will give you a chance to interpret a test matrix

9. 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!!

10. Figure Example Figure 1. Burst strength as a function of time

11. Table Example Table 4. Dependent process variables as a function of the DOE number.

12. Use computer generated schematics Figure 5: Schematic of stressed multi-void tube due to pressure

13. Equations • Use MS Word Equation editor • Number equations sequentially, right justified • See Lab notes

14. Statistical Analysis Review • Mean • Standard Deviation • Sample Size

15. Mean

16. Standard Deviation Simple variance Sample variance Standard deviation of a sample

17. Histogram and normal distribution

18. Standard deviation and data

19. How many samples are enough? n xi xave 1 90 --- --- 2 89 89.50 0.707 3 91 90.00 1.000 4 87 89.25 1.708 5 80 87.40 4.393 6 90 87.83 4.070 7 92 88.43 4.036

20. 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

21. Chauvenet’s criteria • Calculate xave and  for data set • Get dmax/ for the specific sample size from a table • Calculate dmax = (dmax/) ×  • Determine if the most “reject-able” data is larger than this value, d = |xave – xi| • Reject outlying data and then recalculate xave and  for data set

22. Chauvenet’s Example

23. Chauvenet’s Example • 4 (5 data points, n=5) • 2 • 3 Average = 6.2 • 17 • 5 Standard Deviation = 6.14 • Which one to reject? • Technically, examine at them all • Realistically, focus on “17” • For n = 5, reject at ← REJECT 17

24. 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.

25. Uncertainty calculation • Report uncertainty as a % of calculated value

26. 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

27. Formal Report • Abstract • Introduction • Experimental Apparatus • Results and Discussion • Conclusions and Recommendations • Appendices • Uncertainty analysis • Data

28. Abstract (250 words) • Purpose • What was done? • Significant parameters measured or set • Measurement results (summarized) • Quantitative comparisons (i.e., to published values)

29. 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

30. 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

31. 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

32. 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