Employing Deep Neural Networks and High-Throughput Computing for The Identification and Prediction of Vein-Like Structures

In this investigation, we leverage Convolutional Neural Networks (CNNs) to engineer a simple segmentation and recognition algorithm specialized for the delineation of complex, network-like morphologies—often termed "vein-like structures (VLSs)"—in Scanning Electron Microscopy (SEM) imagery. These intricate formations frequently appear during the nitriding treatment of medium- to high-carbon alloy steels. To navigate the multifaceted characteristics of such architectures, we synergize CNN-based methodologies with high-throughput thermodynamic computations via Thermo-Calc. This integration aims to quantify both the theoretical upper bounds and the empirical values of the VLSs. By establishing neural network models for both theoretical upper bounds and empirical measurements, we bridge the gap between thermodynamics and thermo-kinetics in the nitriding process. Applying this amalgamated predictive schema to 8Cr4Mo4V steel, we effectuate a groundbreaking departure from conventional paradigms that exclusively depend on thermodynamic calculation-based diffusion models. The emergent model yields transformative implications for the metallurgical sector, paving the way for the refinement of future nitriding algorithms and enhancements in nitriding methodologies.