Defect Optimation Methods in 3d Printing Process

DOI

10.2991/978-94-6463-864-6_50How to use a DOI?

Keywords

Additive Manufacturing; Defect Optimation; Machine Learning

Abstract

3D printing technology has revolutionized manufacturing with its ability to produce complex components, yet defects such as pores, cracks, deformation, and surface irregularities hinder its widespread adoption. Therefore, in recent years, many defect detection and optimization methods have been designed by researchers related to the above problems. This paper reviews defect optimization methods in 3D printing, focusing on three key approaches: computer vision-based detection, real-time sensor monitoring, and process parameter optimization. Computer vision techniques, leveraging high-resolution imaging and deep learning, enable real-time defect identification and correction during the printing process. Sensor-based systems, including optical and acoustic sensors, provide dynamic feedback to adjust printing parameters. Process optimization balances thermodynamic and mechanical properties to minimize defects. Despite advancements, challenges remain, including complex defect mechanisms, material heterogeneity, and hardware limitations. Future directions emphasize technological integration, material innovation, precise process control, and interdisciplinary collaboration. Improvements to these issues will enhance 3D printing quality and expand its industrial application.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhouyue Yuan
PY  - 2025
DA  - 2025/10/23
TI  - Defect Optimation Methods in 3d Printing Process
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 567
EP  - 582
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_50
DO  - 10.2991/978-94-6463-864-6_50
ID  - Yuan2025
ER  -