raDar /Deformed to formed // Software III M.L.

raDra is a project by MRAC 01 students: Abnaoub Nagy, Andrea Najera, Alberto Martinez, and Grace Boyle during the third software  seminar of the third term. Third software  seminar was taught by faculty Mateusz Zwierzycki, Nikol Kirova.

During the software  seminar , students had to explore ::

 Machine Learning  in particular Supervised machine learning and create the full pipe line from data accusation to a train modeled.










Deformation in the additive manufacturing process with clay is a real problem that takes a lot of time to experiment with so our approach is to feed the computer through neural network and  prediction of drying distortions in robotic clay 3D printing . By that it can reduce the time of testing and the trained model will give the optimized model for printing.


Test Samples


—— the print video—–


4 sets of samples

8 geometries / set

120-200 photos / geometry

Total ~3500 photos as input for photogrammetry models

Data Accusation

Consist of three steps :

A- Scanning througth photogrametry


Align photos
Build dense cloud
Build mesh
Export .obj




B- Orienting


Red CAM model & source of robot toolpath

White Digital model of resulting dried print piece, created via photogrammetry




C- Distortion measurements

| Reorient photogrammetry model & overlay with CAM model

| Front view reveals minor axis / Right view reveals major axis of geometry

| Measure absolute values of layer offset, per layer & per edge

Centre of model located on node in 3D environment, so:

|Left edge:
Shrink → -ve
Swell → +ve

|Right edge:
Shrink → +ve
Swell → -ve

Peeping the date set




Use the trained model to predict

      Thank You