1. Engineering Problem Solving

2. Engineers are problem solvers Industrial Nuclear Computer Science Mechanical Civil Electrical Chemical

3. Engineering Problem Solving Engineers need a strong background in many different technical fields including Physics Mathematics Chemistry Computational science

4. Engineering Problem Solving Successful resolution of engineering problems also requires Common sense Good judgment +=

5. Engineering Problem Solving Engineering solutions often involve balancing and making trade-offs between several competing factors Cost Efficiency Productivity Design Reliability Performance

6. Engineering Problem Solving Define the problem Determine what information is known. Determine what information is needed. Decide which engineering principles apply to the problem. Select an appropriate methodology or solution strategy to apply to the problem. Make simplifying assumptions. Iterate. Test and verify solution.

7. Example Plastic milk-crates, like many other products in use, are designed by "feel". The uncertainty of the effects of unknown factors is resolved by over-dimensioning the crates and, as a consequence, making them heavier. Your company has been hired by the crate manufacturer to improve the design of the crate in an effort to reduce manufacturing costs.

8. Defining the problem Problem definition is often the most difficult phase of engineering problem solving Problems are often ambiguous and/or not clearly specified

9. Problem Definition What is the overall purpose of the problem?

10. Gathering Information Gather relevant information about the problem Examine previous solutions to similar problems Perform experiments (e.g., simulation) Communicate results effectively

11. Collecting Data What information is known? What information must be determined?

12. Selection of Theories and Methods Depends heavily on engineer’s educational background and training Computers are often used to analyze existing data Computers are often used to test different models and theories Many methods need the computing power of today’s PC’s due to the volume of data, the need for graphical or statistical analyses, or the application of mathematical solutions

13. Theories and Methods What fundamental engineering principles apply to this problem?

14. Simplifying Assumptions A theory is an abstraction of how the world works Simplify solution by making simplifying assumptions Analyzing data helps in defining assumptions

15. Iterative solutions Engineering problems are often solved iteratively Problem Statement Is there more problem solving to be done? Analyze problem Generate Solution Test Solution Use Solution End Yes No

16. Testing and Verification Testing and verification is a critical step before any solution is implemented Misplaced decimal points Unit conversion errors (NASA satellite) Impossible to test all feasible solutions Statistical sampling can be very useful!!

17. Solution Generation What will be the overall solution strategy?

18. Example You have been hired by Flights R Us to design an electronic checklist product to be used by general aviation pilots.

19. Engineering Design Define the design objectives Determine what information is known. Determine what information is needed. Decide which engineering principles apply to the design. Select an appropriate methodology or solution strategy to apply to the design. Make simplifying assumptions. Iterate. Test and verify solution.

20. Engineering Design and Computers Outline the basic steps to approach the engineering design problem given. Where would computers and software be used? What type of computer and software would be most relevant to the problem at each step of the problem solving process?

21. Computers and Computing

22. Computers and their applications: Personal digital assistants (PDA’s) Personal computers (PC’s) Workstations Servers Supercomputers Special purpose computers

23. Usage? What is the primary purpose for each type of computer? What are the advantages? What are the limitations?

24. Types of Software Files: Named collection of information stored on a computer Word processing document or spreadsheet Database Drawing Program instructions Programs: Ordered set of instructions that tell a computer what to do Application programs Operating systems

25. General Purpose Applications Spreadsheets Microsoft Excel Database Microsoft Access Web clients (browsers) Microsoft Internet Explorer Netscape Navigator

26. General Purpose Programs Software for developing software C++ Java Visual Basic

27. Operating Systems Collection of programs that Interface with the user Store, organize, and provide access to files Provide access to disks and other devices Start and stop application programs Provide services to application programs Examples Linux Windows

28. Computer Networks Sharing resources May be classified according to Geographic distribution Local area network (LAN) Wide area network (WAN) Interconnection structure (topology) Communication mode employed Speed or data rate of the links

29. ENGR 112 Data Analysis in Excel

30. Engineers and Excel Excel is used extensively by many engineers and in all types of engineering functions – manufacturing, product development, research, marketing and sales Problems become Easier Less time consuming Many summer internships require the use of a spreadsheet tool such as Excel

31. What is Data Analysis? Mathematical and graphical operations that can be performed on experimental data Used to extract the information contained in the data Can significantly affect how information is perceived by decision maker

32. Data Analysis Objective DATA INFORMATION Mean = 93.16 Std Dev = 3.18

33. Data Analysis Choosing and collecting the data Decide what data is needed such as time, temperature, date, equipment number, etc Collect data manually or through automated means such as a scanner, sensors, file transfer, etc.

34. Data Analysis Processing the data Generate useful information The same data set may be used to produce information for different purposes Consider the who needs the data, for what purpose, and how the data will be used.

35. Data Analysis Using the information Involves PEOPLE!! Decision making starts when information becomes available How people use information depends on Intuition Experience Training Interest Ethics

36. Data Analysis Numerical methods Descriptive statistics Measures of central tendency Measures of dispersion Graphical methods Line chart Pie chart Histogram

37. Data Analysis Example Strength testing of materials often involves a tensile test in which a sample of the material is held between two mandrels and increasing force (stress) is applied. A stress-strain curve is generated to provide information about a particular material. Strain is the amount of elongation of the sample divided by the original sample length.

38. Data Analysis Example The stress-strain data taken from a soft, ductile material tested in this way is tabulated to the left.

39. Data Analysis Example

40. Numerical Analysis

41. Numerical Methods There are 2 key descriptors for a set of data (descriptive statistics) Measures of central tendency Mean Median Mode Measures of dispersion Range Variance Standard deviation

42. Central Tendency -- Mean Also known as average Most popular measure of central tendency Where x i = Observation number i n = Total number of observations

43. Central Tendency -- Mean Features Always exists Unique Allows further statistical manipulations, e.g. confidence intervals Limitations Affected by the presence of unusually small or large values (called outliers)

44. Central Tendency -- Median Middle observation within a data set when the observations are arranged in increasing order If number of values (n) in data set is odd, then the median is the middle observation If number of values (n) in data set is even then Median = ( x n/2 + x n/2+1 ) /2

45. Median Examples Example #1 32.3, 42.3, 44.5, 31.3, 42.2 Median = Example #2 31.3, 32.3, 42.2, 42.3, 44.5, 47.5 Median =

46. Central Tendency -- Median Features Always exists Unique Not affected by extreme values Easier to calculate Limitations Not always representative of entire data set Size of data set does not impact weighting of values

47. Central Tendency Mean vs. Median If distribution of values is Left-skewed  Mean < Median Right-skewed  Mean > Median Symmetrical  Mean  Median

48. Central Tendency -- Mode Value that occurs more often than any of the others in a data set Does not always exist Example: Scores from a test Is not necessarily unique, i.e. a data set can have more than one mode = 2 modes  Bimodal > 2 modes  Multimodal 91 92 89 78 65 100

49. Central Tendency -- Mode Applicable to both quantitative and qualitative data Particularly useful in marketing and inventory considerations

50. Dispersion Consider the following problem Canned mixed nuts suppliers Sample five cans and count # of peanuts Supplier A: 21 20 19 20 20 Supplier B: 29 11 10 33 17 Who would you buy from? Why?

51. Dispersion -- Range Difference between the largest and smallest values in a data set Supplier A: 21 20 19 20 20 Range = Supplier B: 29 11 10 33 17 Range =

52. Dispersion -- Variance Measures how a set of measurements fluctuate relative to the mean of the data set Shortcut

53. Dispersion – Standard Deviation What is the problem with the variance? It has different units of measurement (e.g., cm 2 ) To return data to its original units Standard deviation =