In this lab we try to discover if by adding weight and decreasing weight would there be a difference in power levels used.For our first experiment we started by comparing the acceleration vs. the mass. For our experiment we set up the pulley system with our robot and then connected the weights to the string which was attached to the wheel of the robot. Our first trial consisted of lifting .25kg of weights and recording the acceleration in a excel sheet. We then repeated this experiment 9 times decreasing the weight every trial by .04kg. Once we received all our data we put it into a scatter plot to see the correlation between acceleration and mass. As shown in the graph below, the r²=.923 which is a very strong correlation. From this information we were lead to believe that mass has a direct effect to the acceleration of the robot.
For our second experiment we compared the power used vs. the power level. For our experiment we left the set up of the pulley system together. This trial consisted of leaving the weight of .25kg constant while changing the power level at which the robot will lift the weights. We changed the power level four times starting with 75watts and working our way down to 30 watts. After we recorded all of our data we put it into a scatter plot to see the correlation between the power used and the power level. As shown in the graph below, the r²=.9722 which is an even stronger correlation than the previous experiment of mass and acceleration. From this information we concluded that there is a direct relationship between the amount of power used and the percentage of the power level.
From the previous experiment, we used the data from the changing of power levels and compared it the the acceleration data. What we hope to accomplish from comparing these two variables is to see if there is a direct correlation when increasing power levels and the robots acceleration of lifting the weights. What we found from the data is that the two have have a very strong correlation of r²=.9929 which almost forms a straight line. So from this our hypothesis is proven true, changing power levels do directly effect the acceleration of lifting weights.
For our last trial experiment we compared the relationship between power used and the percentage of the power level. In order to do this experiment we kept the data from the last experiment of different power levels and used it in this one. In order to get the power used we had divide the potential energy by time in order to then divide mgh/time. After figuring all of the calculations out, we came up with our data and placed it in the scatter plot below. From the scatter plot it shows how the power level and power used are correlated with an r² value of .9722. This shows that when the power is increased so is the power used.
Your experiment offers some fascinating insights into the relationship between weight and power consumption, especially in robotics and mechanical systems. The findings raise thought-provoking questions about the efficiency of power usage in machines when handling varying loads. As technology continues to advance, how can we optimize systems to reduce power consumption while maintaining performance? Could this understanding be applied beyond lab settings, such as in industrial equipment or even everyday objects?
It’s also intriguing to consider how this principle might impact businesses in industries that rely on portable machinery, like event services. For instance, at The Lavatory, which provides portable restroom trailers, efficient power usage could play a crucial role in ensuring sustainable operations, especially in remote or off-grid locations. The balance between power and performance is key in such sectors, making your research highly relevant across various industries.
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