Since I am trying out HIPS as a support material for printing ASA filament, it seems wise to repeat the flow rate calibration procedure I performed for Polymaker’s PolyLite ASA and the Kodak HIPS filament. There’s no readily available filament profile for HIPS, so let’s start with the Generic ASA filament profile. When we use HIPS as a support for ASA, it has to have compatible process parameters anyway. The baseline is 240°C nozzle and 100°C bed temperatures and a flow ratio of 0.95. Go look at that post to see how we got here.

First pass

This is the rather comical result of the first pass with HIPS and automatic flow calibration disabled:

Yikes, what a hot mess. And I may be interpreting this mess wrong, but I’m going with the +20 coupon as the most improvement, although I’m sure the bed temperature played a role here, and I’m going to up this parameter.

\[flowratio*(100 + modifier)/100\] \[0.95*(100 + 20)/100 = 1.14\]

That’s a pretty big change. Let’s change the filament profile to 1.14, try this again, and see if we have the same disaster. And I’m bumping the bed temperature from 100°C to 105°C to help with adhesion.

2nd First Pass

This is fun. Except that, as expected, +20 is a bit scary. It has chest hair!

And taking a look at the back, we can see more interesting stuff:

While the top of -20 looked and felt the best, the bottom tells a different story. -15 or -10 do not have pronounced pinholes where the infill meets the wall. My compromise is the -15 coupon in the bottom row middle.

\[flowratio*(100 + modifier)/100\] \[1.14*(100 -15)/100 = 0.969\]

0.969 or 0.97 is our new value for the flow rate for this particular HIPS filament. The rest is basically the ASA profile I created.

Don’t forget: Disable flow calibration in the print job settings.

Again, hope this is helpful to somebody out there, if nothing else, to understand what’s possible. It appears the Bambu Lab auto flow rate calibration doesn’t always get a perfect calibration and can benefit from calibration with the OrcaSlicer calibration models for fine-tuning. No, this is not meant as a critique but more of a reminder of what is possible. It is a great feature because almost everything I’ve tried works out of the box.

But you can do better if you invest the effort, and my examination of more data points is much more complicated than observing the extrusion of lines. Even if I saw what data this calibration generated, I don’t think it would make any difference in what this calibration model can teach us.

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