Statistically-based Bed Compensation and Calibration

  • Hello,

    Had an interesting experience with the Smart Effector the other day. I ran bed calibration and got something like this:

    (Shamelessly stolen from TLeTorneau in another thread)

    What's interesting is that this shows some real mechanical drift of the sensor over time. I had the same problem on the Smart Effector. I doubt that this is an artifact of the print bed, especially since when I changed directions, the lines appeared in the other direction.

    I think an ideal bed probe system would be to implement the following:

    • Randomly determine the probe point order.
      • This would help eliminate sensor drift from the final results.
      • Alternatively, you could probe in reverse directions (although randomly would be better)
    • Perform 3+ measurements at the same point (at random points in time)
      • This gives you mean standard error reduction
    • Implement a localized flatness smoothing
      • Sharp bumps are more likely to be errors than real changes.
      • False sharp bumps can produce problems / deformations in the final print

    Bottom line is that with the statistical methods mentioned above, I think it's possible to achieve a 3X decrease in calibration sensor error. For mechanically based sensors, this could be a real boon and help achieve the accuracy needed for printing.

  • Have you checked out the A and S parameters of set probe type?

    On the upside for the current method testing in a specific order does give you a fighting chance of driving out sources of error. Randomising the order could potentially hide these patterns giving the false impression of it just being a noisy measurement?


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