Uncertainty-driven trustworthy defect detection for high-resolution powder bed images in selective laser melting

Abstract

Selective laser melting (SLM) is known as one of the most promising metal additive manufacturing technologies, and how to ensure its consistent quality is still a main challenge, which urgently needs to be addressed. The metal powder spreading quality, as the first stage of SLM, has a direct impact on the subsequent forming and it is necessary to be monitored. However, existing deep learning-based methods for monitoring powder spreading quality often suffer from the problem of unreliability. The high-resolution property of powder bed images is often ignored, and the predicted results are not well evaluated. To address the above issues and achieve trustworthy defect detection, this paper proposes an uncertainty-driven trustworthy defect detection method for high-resolution powder bed images in SLM. A super resolution module based on ISDNet is first proposed to refine the upsampling process. Checkpoint ensemble is adopted for better achieving model uncertainty estimation only requiring a single training process, feature reuse helps reduce the computational load, and temperature scaling is used to further calibrate each ensemble member to get a more precise uncertainty. Besides, we design an uncertainty-driven model improvement method to further improve defect detection performance for regions with high uncertainty. Experiments demonstrate the effectiveness of our proposed method, and our work achieves a trustworthy defect detection for high-resolution powder bed images in SLM.