{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T23:49:29Z","timestamp":1789516169288,"version":"build-2803163510"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Purdue University School of Materials Engineering Center for Surface Engineering and Enhancement"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>This work presents a dynamic flowsheet model that predicts residual stress from shot peening. The peening medium is characterized by size and shape, and evolves dynamically with abrasion, fracture, classification, and replenishment. Because particle size and impact location vary stochastically, the resulting residual stress field is spatially heterogeneous. Residual stress fields are predicted in real time through a convolutional long short-term memory (ConvLSTM) neural network trained on finite element simulations, enabling fast, mechanistically grounded prediction of surface stress evolution under industrial shot peening conditions. We deploy the model in a pair of 10,000-cycle production peening case studies, demonstrating that media recharge strategy has a measurable effect on residual stress outcomes.<\/jats:p>","DOI":"10.3390\/ma19010009","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T03:06:02Z","timestamp":1766718362000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural Network-Enabled Process Flowsheet for Industrial Shot Peening"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0965-5310","authenticated-orcid":false,"given":"Langdon","family":"Feltner","sequence":"first","affiliation":[{"name":"School of Materials Engineering, Purdue University, 701 W Stadium Ave., West Lafayette, IN 47901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1510-8105","authenticated-orcid":false,"given":"Paul","family":"Mort","sequence":"additional","affiliation":[{"name":"School of Materials Engineering, Purdue University, 701 W Stadium Ave., West Lafayette, IN 47901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cirpj.2020.02.002","article-title":"Characterising the Digital Twin: A systematic literature review","volume":"29","author":"Jones","year":"2020","journal-title":"CIRP J. 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Met."},{"key":"ref_11","unstructured":"Balan, K. (2025, April 08). Are You Still Using MIL-S-13165, The Shot Peener Magazine|Summer, Available online: https:\/\/www.shotpeener.com\/library\/pdf\/2020026.pdf."},{"key":"ref_12","unstructured":"Mort, P., and Feltner, L. (2022). Characterization of Shot Size and Shape Distribution, Metal Finishing News."},{"key":"ref_13","unstructured":"Feltner, L., Gruninger, M., Canty, T., and Mort, P. (2025, April 10). Characterization of Particle Size and Shape Distributions for Shot Peening Media, The Shot Peener Magazine|Spring, Available online: https:\/\/www.shotpeener.com\/library\/pdf\/2024011.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.advengsoft.2009.03.013","article-title":"On the potential applications of a 3D random finite element model for the simulation of shot peening","volume":"40","author":"Miao","year":"2009","journal-title":"Adv. Eng. 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