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Discussion
1. What could you have done better in your experiment design and setup?
Having a fixed ruler or measuring tape on the wall could have increased the accuracy
and consistency of the results. A camera tracking software could have been more
accurate since it eliminates human error.
2. Discuss your rationale for the model you selected. Describe any assumptions or
simplifications this model makes. Include external references used in selecting
or understanding your model.
The model selected will help determine which material thickness is ideal for stiffer links,
especially under load. I collected data for 10 mil and 30 mil fiberglass sheets using
identical weights for both.
The assumptions made are:
○ Other layers do not contribute to stiffness
○ We have a symmetric laminate.
3. Justify the method you selected (least squares, nonlinear least squares,
scipy.optimize.minimize(), Evolutionary algorithm, etc. ) for fitting experimental
data to the model, as well as the specific algorithm used.
The method selected for fitting the experimental data was least square optimization in
order to find a linear relationship between load and link flexibility. The data collected
appear to be nearly linear and least square approximation helped provide useful data.
4. How well does your data fit the model you selected? Provide a numerical value
as well as a qualitative analysis, using your figure to explain.
The data helped us determine that the 30 mil fiberglass sheet is a better material to use
in order to reduce flex in our links. From the data we also determined that the flexibility
is linearly related to the load on the end of the link. The 30 mil sheet flexes at a much
slower rate than the 10 mil sheet. From the data we can find the stiffness of the material
through the following equation: