Online Measurement for Parameter Discovery in FFF
In order to automatically guess at print parameters (flowrate and nozzle temperatures), we developed an extruder that features a filament sensor (to detect real flowrates) and a pressure sensor. We used it to measure a swath of that parameter space using a simple routine, fit that data to a predictive function, and use a meta-heuristic to extract real parameters from those functions. We were then able to automatically determine print parameters for a number of new filaments that we had not previously tested.
Above is our extruder, featuring a loadcell to measure pressure (C), a filament sensor (A) that measures filament width and linear feedrate, and a COTS hotend (D) and drive gears (B).
We collect data with a simple routine that sets the nozzle at its maximum temperature, then begins flowing plastic at a set rate, and turns off the heater. Pressure and temperature data are simultaneously collected as the nozzle cools (and as pressure increases).
We can expand this fit into a full contour of the parameter space, and then select operating points using a meta heuristic.
Using this method, we were able to produce a series of test articles using filaments that we had not previously tested.
In this Repo
Analysis and Data
We have data an analysis in the analysis/ folder, and data sets stored in data/. Analysis codes (Jupyter Notebooks) run with these data.
Data there is organized into material-nozzleSize folders, with raw data in .json format and cleaned data sets available as python pickles, which import as pandas dataframes. Data was cleaned using this script and analyzed using this one.
Experimental System
The system folder contains the javascript controller as well as firmwares for the relevant hardware.
The Paper
A draft of the paper is located here.