Clad Characteristic

"Clad characteristic" research focuses on predicting and understanding the properties of deposited material layers (clads), crucial in various applications like metal additive manufacturing and audio deepfake detection. Current research employs machine learning, particularly deep neural networks and contrastive learning, often combined with dimensionality reduction techniques like functional PCA, to model complex relationships between processing parameters and clad features (e.g., geometry, quality, or audio authenticity). These advancements aim to improve process control, enhance the robustness of detection systems, and ultimately lead to more efficient and reliable manufacturing processes and security measures.