A naukri.com initiative
Bioengineer
6d
20
Image Credit: Bioengineer
Machine Learning Predicts Bead Geometry in Additive Manufacturing
- A study by Vanerio, Guagliano, and Bagherifard introduces a novel approach using machine learning to predict bead geometry in fused granulate fabrication for large-format additive manufacturing.
- The methodology aims to enhance consistency, structural integrity, and efficiency in additive manufacturing processes, particularly for large-scale applications.
- Traditional methods of calibrating bead dimensions involve trial-and-error processes that are time-consuming and costly, especially in large manufacturing settings.
- The research team utilized machine learning and image-based analysis to dynamically predict bead geometry, shifting from empirical adjustments to data-driven predictions.
- Convolutional neural networks (CNNs) were employed to analyze real-time data and anticipate geometric outcomes of extruded beads, offering more precise predictions than traditional models.
- The model demonstrated generalization across materials and settings without needing retraining, enabling real-time monitoring for process optimization.
- The study validated the model against experimental measurements, showcasing its effectiveness in both controlled and complex manufacturing environments, advancing automated quality control systems.
- The research's implications extend beyond granulate fabrication, suggesting broader applications in various additive manufacturing technologies and industries.
- AI-driven predictive tools can significantly reduce defect rates, accelerate product development, and ensure superior mechanical reliability in critical applications like aerospace and automotive industries.
- The study's scalable approach balances accuracy and efficiency, offering practical integration into industrial equipment for enhanced production control.
Read Full Article
1 Like
For uninterrupted reading, download the app