Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations
As Additive Manufacturing (AM) evolves from prototyping to full-scale production, improving geometric accuracy becomes increasingly critical, especially for applications requiring high dimensional fidelity. This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/5/382 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327024584261632 |
|---|---|
| author | Moustapha Jadayel Farbod Khameneifar |
| author_facet | Moustapha Jadayel Farbod Khameneifar |
| author_sort | Moustapha Jadayel |
| collection | DOAJ |
| description | As Additive Manufacturing (AM) evolves from prototyping to full-scale production, improving geometric accuracy becomes increasingly critical, especially for applications requiring high dimensional fidelity. This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D printed parts produced by Fused Filament Fabrication (FFF), a widely used material extrusion process in which thermoplastic filament is heated and deposited layer by layer to form a part. Our method relies on a Convolutional Neural Network (CNN) trained to predict a systematic deviation field based on 3D scan data of a sacrificial print. These scans are acquired using a structured light 3D scanner, which provides detailed surface information on geometric deviations that arise during the printing process. The predicted deviation field is then inverted and applied to the digital model to generate a compensated geometry, which, when printed, offsets the errors observed in the original part. Experimental validation using a complex reference geometry shows that the proposed compensation method achieves an 88.5% reduction in mean absolute geometric deviation compared to the uncompensated print. This significant improvement underscores the CNN’s ability to generalize across geometric features and capture systematic deformation patterns inherent to FFF. The results demonstrate the potential of combining 3D scanning and deep learning to enable adaptive, data-driven compensation strategies in AM. The method proposed in this paper contributes to reducing trial-and-error iterations, improving part quality, and facilitating the broader adoption of FFF for precision-demanding industrial applications. |
| format | Article |
| id | doaj-art-cd6fd1a4de824761a9d2f8d50aea9314 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-cd6fd1a4de824761a9d2f8d50aea93142025-08-20T03:47:58ZengMDPI AGMachines2075-17022025-05-0113538210.3390/machines13050382Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric DeviationsMoustapha Jadayel0Farbod Khameneifar1Department of Mechanical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, CanadaDepartment of Mechanical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, CanadaAs Additive Manufacturing (AM) evolves from prototyping to full-scale production, improving geometric accuracy becomes increasingly critical, especially for applications requiring high dimensional fidelity. This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D printed parts produced by Fused Filament Fabrication (FFF), a widely used material extrusion process in which thermoplastic filament is heated and deposited layer by layer to form a part. Our method relies on a Convolutional Neural Network (CNN) trained to predict a systematic deviation field based on 3D scan data of a sacrificial print. These scans are acquired using a structured light 3D scanner, which provides detailed surface information on geometric deviations that arise during the printing process. The predicted deviation field is then inverted and applied to the digital model to generate a compensated geometry, which, when printed, offsets the errors observed in the original part. Experimental validation using a complex reference geometry shows that the proposed compensation method achieves an 88.5% reduction in mean absolute geometric deviation compared to the uncompensated print. This significant improvement underscores the CNN’s ability to generalize across geometric features and capture systematic deformation patterns inherent to FFF. The results demonstrate the potential of combining 3D scanning and deep learning to enable adaptive, data-driven compensation strategies in AM. The method proposed in this paper contributes to reducing trial-and-error iterations, improving part quality, and facilitating the broader adoption of FFF for precision-demanding industrial applications.https://www.mdpi.com/2075-1702/13/5/382additive manufacturingfused filament fabricationconvolutional neural networkgeometric accuracy3D printinggeometric compensation |
| spellingShingle | Moustapha Jadayel Farbod Khameneifar Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations Machines additive manufacturing fused filament fabrication convolutional neural network geometric accuracy 3D printing geometric compensation |
| title | Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations |
| title_full | Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations |
| title_fullStr | Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations |
| title_full_unstemmed | Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations |
| title_short | Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations |
| title_sort | increasing 3d printing accuracy through convolutional neural network based compensation for geometric deviations |
| topic | additive manufacturing fused filament fabrication convolutional neural network geometric accuracy 3D printing geometric compensation |
| url | https://www.mdpi.com/2075-1702/13/5/382 |
| work_keys_str_mv | AT moustaphajadayel increasing3dprintingaccuracythroughconvolutionalneuralnetworkbasedcompensationforgeometricdeviations AT farbodkhameneifar increasing3dprintingaccuracythroughconvolutionalneuralnetworkbasedcompensationforgeometricdeviations |