Defect detection in additive manufacturing using image processing techniques
Bibliographic record
Abstract
Additive manufacturing (AM) allows to produce parts layer by layer from an STL file. It is then possible through this technology to obtain customized geometries and complex shapes at a lower cost. However, these shapes pose problems of defect control, microstructure, residual stresses, and deformations in parts. This study aims to develop an efficient method allowing defect detection while printing pieces using Fused Deposition Modelling (FDM). The monitoring system contains a camera acquisition system for automatic image capture of filament layers deposited on the print bed. Various monitoring techniques have been simulated to achieve an optimal defect correction solution. Material excess and deficiency are detectable in the layer of actual printed parts. Defects are controlled and compared to original part obtained from Computer Aided Design (CAD). An app-designer application was created in this regard. It displays the image reference generated from the G-code, the layer image captured by the camera, and returns the error percentage in the printed layers. The developed method of surface calculation has shown its efficiency in detecting the lack and excess of material, which has an accuracy of 1.07%. This method allows users to stop and monitor printing to save cost, material, and time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".