Developing a function/algorithm to negate voltage change

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The discussion focuses on developing an algorithm to negate voltage changes caused by temperature fluctuations in a composite material experiment. The goal is to ensure that voltage readings remain constant when no additional weight is applied. The user seeks advice on creating a function that can accurately subtract temperature effects from voltage data collected over two days. One suggested approach involves identifying periods of abrupt voltage changes when weight is added and using surrounding data points to interpolate and maintain continuity in the dataset. The conversation emphasizes the need for accurate calibration to isolate the effects of temperature on voltage readings.
benanderson08
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Hey guys,

So in one of my classes we are studying composite materials. We did an experiment where a piece of composite material was anchored at each of it's ends and the temperature was changed throughout the day to simulate the varying outdoor temperature. This gives you a center deflection that is read by a laser and the laser outputs a voltage value.

The problem is that the deflection is meant to measure added weight on top of the composite material at a later date, so the voltage change due to temperature change has to be subtracted from the voltage reading i.e. the original setup with just the ends anchored and no additional weight would yield a constant voltage reading.

So the goal of this assignment is to develop a function/algorithm that would be able to subtract the effects of temperature from the voltage reading in order to keep the voltage constant when there is zero additional weight. This function/algorithm has to apply to both of the days of data attached.

I've tried a few models myself, but if someone could help me with this I would really appreciate it. Any advice would be great as well.

Thank you!
 

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I haven't looked at your data sets, but my first thought is to apply the calibration at the times when the weight is added. So look for abrupt changes and treat those periods of abrupt change as data "holidays". Then extrapolate from the last few data points on either side of the holiday to fill in the missing data and reconnect the pre-holiday and post-holiday data sets into a continuous plot.
 
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