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I am fairly new to machine learning. I have been in mechanical simulation field for the past 7-8 years, I realise there are potential areas which I have been doing the same thing day in and day out, but ML can automate those activities. I need ideas on one such activity:

I have a system which rotates from a angle $\theta_1$ to a different angle $\theta_2$. The system starts from rest and should end at the particular angle at rest. In between, initially an operator accelerates it until he realizes at a certain time that he need to deccelerate the system to make it stop at the particular angle. My job is to determine the time point when the operator should start deccelerating (using I get the time point by trial and error). The input of the acceleration/ decceleration is coming from a motor whose torque v time data is there with me. I have plenty of acceleration-velocity-displacement-time graph with me.

Is this possible to be automated using ML? The problem might not be very exact right now, but as ideas starts pouring in, I can enrich the problem and solve it. Thanks for participating.

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  • $\begingroup$ This would be better solved using reinforcement learning (RF) rather than machine learning (ML). RF would learn exactly which actions to take at a point in time to reach a goal, while ML might tell you which course was better in the historical data, which is not the same. $\endgroup$
    – user2974951
    Commented Dec 9, 2022 at 10:46

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Yes, it is possible to automate the activity you have described using machine learning. The specific approach you take will depend on the details of your system and the data you have available, but one potential approach could be as follows:

  1. Collect data on the acceleration/decceleration of the system, as well as the torque and time data from the motor.

  2. Use this data to train a machine learning model that can predict the time at which the operator should start deccelerating the system in order to stop at the desired angle. This could be a regression model that predicts the time point based on the acceleration/decceleration and torque data.

  3. Test the trained model on a set of unseen data to evaluate its performance and determine if it is able to accurately predict the time point at which decceleration should start.

  4. If the model performs well, use it to automate the process of determining the time point at which decceleration should start. This could involve feeding the model with new acceleration/decceleration and torque data and using the predicted time point to control the decceleration of the system.

Again, this is just one potential approach, and there may be other ways to solve this problem using machine learning. It may be worth experimenting with different approaches and algorithms to see which one provides the best results. Additionally, you may want to consider incorporating additional features or using more advanced modeling techniques to improve the accuracy of your predictions.

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  • $\begingroup$ Thanks for the idea. I will start working on this approach. $\endgroup$
    – s6292_1997
    Commented Dec 9, 2022 at 5:16

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