Automated Process Monitoring in 3D Printing Using Supervised Machine Learning
| Autor: | Shing I. Chang, Ugandhar Delli |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
3d printed business.industry Computer science media_common.quotation_subject Process (computing) 3D printing Image processing 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre Industrial and Manufacturing Engineering Support vector machine 020901 industrial engineering & automation Artificial Intelligence Quality monitoring Quality (business) Artificial intelligence 0210 nano-technology business computer media_common |
| Zdroj: | Procedia Manufacturing. 26:865-870 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2018.07.111 |
| Popis: | Quality monitoring is still a big challenge in additive manufacturing, popularly known as 3D printing. Detection of defects during the printing process will help eliminate waste of material and time. Defect detection during the initial stages of printing may generate an alert to either pause or stop the printing process so that corrective measures can be taken to prevent the need to reprint the parts. This paper proposes a method to automatically assess the quality of 3D printed parts with the integration of a camera, image processing, and supervised machine learning. Images of semi-finished parts are taken at several critical stages of the printing process according to the part geometry. A machine learning method, support vector machine (SVM), is proposed to classify the parts into either ‘good’ or ‘defective’ category. Parts using ABS and PLA materials were printed to demonstrate the proposed framework. A numerical example is provided to demonstrate how the proposed method works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|