[ot][spam] ai-guided 3d printing

Undescribed Horrific Abuse, One Victim & Survivor of Many gmkarl at gmail.com
Fri Apr 21 08:35:34 PDT 2023

how i found papers: i looked straight for papers, but no info. looked
for news, which mentioned commercial products. i looked for the
commercial products, but no info. i looked for papers that mentioned
commercial products and found things this way.

as if were 14 (i’m roughly 40) i showed off a gear assembly i 3d
printed to my father. he said i should make a rocket like he had seen
in the new. well, i’m all about shooting for the moon. the rocket from
the news was ai-designed. due to dissociation, i’m only stealing
mainstream approaches and not pushing the edge of algorithm training
(until it’s more common to?)

i’m presently experiencing inhibition (due to resisting it
aggressively again) and have stopped playing video games [again?] so
this seemed like something that reasonably crossed productivity and

here’s a recent paper on ai and 3d printing:
Computer Vision Based Analysis for Fused Filament Fabrication Using a
G-Code Visualization Comparison

Abstract [edited down for conciseness and clarity]
a probability of 41.1% remains that the printed part will have errors.
This investigation provides an account of a camera based monitoring
system developed to detect complex problems.
- Image segmentation was used to remove the background of the printed
part and the result was compared to a visualization of the G-Code.
By using an exclusive-or method it was possible to determine
differences, which can indicate defects. Depending on the similarity,
the printing process can be canceled promptly.
Tests have demonstrated that this method works reliably even under
changing lighting conditions in most cases but can lead to poor
segmentation due to shadows being cast in the infill. The application
is also able to recognize differences when printed parts detach or
layers have shifted if they are not covered by lower layers. The use
of a light source on top of the 3D printer and additional cameras,
beside the build plate, could solve both problems in the future.

comments: addresses detection of failure, which is possibly a part of
a potential larger problem of ai-designed g-code. the approach of
image segmentation appears simple, one could probably finetune a
popular pretrained model or 3d architecture to outperform this method.

here’s another similar-looking recent paper:
https://run.unl.pt/handle/10362/149946 Building a Fused Deposition
Modelling (FDM) 3D Printing Visual Defect Detection System, Part 1:
Creation of a Dynamic Imaging System, a Pixel-wise Segmentation
Dataset with a Hybrid Synthetic Data Creation Method, and a Semantic
Segmentation Algorithm with the SegFormer Deep Learning Model

Abstract: 	As a part of an effort to develop a surface defect
detection system for FDM 3D printed objects, this work project studies
the application of the SegFormer network to semantically segment 3D
printed objects. The project also showcases an affordable and
accessible imaging system designed for the surface defect detection
system, to support the decisions made during the segmentation task and
to be used to evaluate the segmentation models. To achieve this, the
first-ever pixel-wise annotation dataset of 3D-printed object images
was created. Model-O1, a SegFormer MiT-B0 model trained on this
dataset with minimal data augmentation resulted in an
Intersection-over-Union score of 87.04%. A synthetic data creation
method that caters to the nature of 3D printed objects was also
proposed, which expands upon existing synthetic data creation methods.
The model trained on this dataset, Model-A2, achieved an IoU score of
89.31%, the best performance achieved among the models developed in
this project. During the evaluation of the model based on the
inference results, Model-A2 was also identified to be the most
practical model for building a surface defect detection system.

comments: this paper references a 3d printed imagery dataset which
sounds quite helpful if public.

it’s a little confusing to me that it discusses “synthetic data
creation” but is only addressing defect detection and not g-code
synthesis. this is because i searched for the term “spaghetti
detective” which is a corporate-built defect detector.

the seed article i started with was
https://www.hubs.com/blog/ai-assisted-3d-printing/ . it may be
productive to look for other phrases from the article.

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