Additive Manufacturing (AM) is transforming the manufacturing sector by enabling efficient production of intricately designed products and small-batch components. However, metal parts produced via AM can include flaws that cause inferior mechanical properties, including reduced fatigue response, yield strength, and fracture toughness. To address this issue, we leverage convolutional neural networks (CNN) to analyze thermal images of printed layers, automatically identifying anomalies that impact these properties. We also investigate various synthetic data generation techniques to address limited and imbalanced AM training data. Our models' defect detection capabilities were assessed using images of Nickel alloy 718 layers produced on a laser powder bed fusion AM machine and synthetic datasets with and without added noise. Our results show significant accuracy improvements with synthetic data, emphasizing the importance of expanding training sets for reliable defect detection. Specifically, Generative Adversarial Networks (GAN)-generated datasets streamlined data preparation by eliminating human intervention while maintaining high performance, thereby enhancing defect detection capabilities. Additionally, our denoising approach effectively improves image quality, ensuring reliable defect detection. Finally, our work integrates these models in the CLoud ADditive MAnufacturing (CLADMA) module, a user-friendly interface, to enhance their accessibility and practicality for AM applications. This integration supports broader adoption and practical implementation of advanced defect detection in AM processes.

本研究针对金属增材制造中存在的缺陷问题，通过卷积神经网络（CNN）分析打印层的热图像，自动识别影响机械性能的异常。研究表明，使用生成对抗网络（GAN）生成的合成数据能够显著提高缺陷检测的准确性，同时，所提出的去噪方法有效提升了图像质量，确保了可靠的缺陷检测。这一工作最终集成在CLADMA模块中，增强了先进缺陷检测技术在增材制造中的可接受性和实用性。

金属增材制造缺陷检测的可扩展人工智能框架