engineering & economaic
sb_us
TermProject421.example.docxTerm Project
Total Points: 5
Due date: 05/01/2018
Select an experiment of your choice. Complete following steps of the guidelines for designing experiments given in Section 1.4 of the book:
1. Recognition of and statement of the problem
2. Selection of the response variable
3. Choice of factors, levels, and range
4. Choice of experimental design
5. Performing the experiment
6. Statistical analysis of data
7. Conclusions and recommendations
Finally, prepare a comprehensive report documenting all the 7 steps listed above. Submit your report in the word document format on the Blackboard.
Solution:
Here is the following scenario: "An experimenter from the process engineering group comes to you and says: “We are manufacturing impellers that are used in a jet turbine engine. To achieve the claimed performance objectives, we must produce parts with blade profiles that closely match the engineering design requirements. I want to study the effect of different tool vendors and machine set-up parameters on the dimensional variability of the parts produced on the machines in our CNC-machine center.”
Now, we will perform the first three phases of the experiment design process:
Recognition and Statement of the problem Objective : For machined titanium forgings, quantify the effects of tool vendor; shifts in a-axis, xaxis, y-axis, and z-axis; spindle speed; fixture height; feed rate; and spindle position on the average and variability in blade profile for class X impellers,
Response Variables
It is also known as response Variables
Choice of factors, levels and Range
Experimental design objectives:
Experimental design objectives are listed here:
1. Comparative objective
2. Screening objective
3. Response surface objective
4. Optimizing responses when factors are proportions of a mixture objective
5. Optimal fitting of a regression model objective
Following is the summary table for choosing an experimental design for comparative, screening, and response surface designs:
|
No. Of factors |
Comparative objective
|
Screening objective
|
Response surface objective
|
|
|
1 |
- |
- |
||
|
2 - 4 |
| |||
|
5 or more |
|
Screen first to reduce number of factors |
Statistical analysis of data Now moving to statistical analysis of data, first we will collect quantitative data, once we collected Qualitative data then we will have a lot of numbers for statistical analysis of the data. Therefore, now we will sort out some statistical analysis.
There is a many possible techniques that we may use: The most common techniques used for summarizing is using graphs, specially bar charts, which show every single data point in order, or histograms, which are bar charts grouped into broader categories.
For example, use three sets of data, grouped by four categories. This might, for example, be men, women, and ‘no gender specified, grouped by age categories 20–29, 30–39, 40–49 and 50–59.
Histogram is a line chart and it plots each data point and also joins them with a line. This is same data as we use in the bar chart are displayed in a line graph below:
Here is a pie chart, we can also display grouped data, such as this one:
We also calculate statistics by applying following formulas: 1- Mean
2- Standard deviation
3- Regression
4- Sample Size Deamination
5- Hypothesis Testing
Conclusion and Recommendations In this venture, we concentrated on basic question of experimental outline/design and the related analytical methodologies like choice of the response variable, Choice of components, levels, and range and Statistical examination of information that can be utilized to draw biologic designs. The above case has generally drawn on singular investigations in which data sets have been examined. Besides, any of these techniques can be connected more particularly to bigger accumulations of information than those in singular examinations.
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