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These intricate formations frequently appear during the nitriding treatment of medium\u2010 to high\u2010carbon alloy steels. To navigate the multifaceted characteristics of such architectures, CNN\u2010based methodologies are synergized with high\u2010throughput thermodynamic computations via Thermo\u2010Calc. This integration aims to quantify both the theoretical upper bounds and the actual values of the VLSs. By establishing deep neural network models for both theoretical upper bounds and actual measurements, the gap between thermodynamics and thermokinetics in the nitriding process is bridged. Applying this amalgamated predictive schema to 8Cr4Mo4V steel, a groundbreaking departure from conventional paradigms that exclusively depend on thermodynamic calculation\u2010based diffusion models is effectuated. 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.<\/jats:p>","DOI":"10.1002\/aisy.202400260","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T03:25:41Z","timestamp":1727148341000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Employing Deep Neural Networks and High\u2010Throughput Computing for the Recognition and Prediction of Vein\u2010Like Structures"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2135-6853","authenticated-orcid":false,"given":"Junbo","family":"Niu","sequence":"first","affiliation":[{"name":"School of Material Science &amp; Engineering Harbin Institute of Technology  Harbin 150001 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