This code base was archived on March 14, 2023. This represents a mid-stream snapshot of the development cycle of a machine learning approach to predicting the line widths resulting from an EHD printing process. Development of the image analysis and dataset construction routines continues in onakanob/ehd-dataset. Machine learning models and evaluation continues in onakanob/ehd-ml.
Image analysis for ex-situ characterization of ehd-printed patterns
- Use align_pattern.py to set the offset and angle for a mosaic image so that the EHD toolpath pattern lines up with the printed pattern
- Use the GUI in place_patches.py to place an image patch over each isolated print pattern
- Run parse_patches.py to run image analysis on each patch, extracting metrics.
Section is incomplete
- sklearn
The EHD_Loader object holds multiple training datasets in the loader.datasets array, each a dataframe containing waveforms and measurements from a single experiment. When returning a dataset, the "xtype" and "ytype" arguments control how the X and Y variables (input and supervised output, respectively) will be formatted. The following options are available:
- vector
- wave
- last_wave
- last_vector
- normed_squares
- v_normed_squares
- area
- print_length
- max_width
- mean_width
- obj_count
- jetted
- jetted_selectors
- MLE
- cold_RF
- cold_MLP
- only_pretrained_RF
- only_pretrained_MLP
- MLE_class
- cold_RF_class
- cold_MLP_class
- only_pretrained_RF_class
- only_pretrained_MLP_class