Article(id=1319726359962833090, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726331013742638, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20250397, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747670400000, receivedDateStr=2025-05-20, revisedDate=1748707200000, revisedDateStr=2025-06-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1792576883544, onlineDateStr=2026-10-21, pubDate=1760889600000, pubDateStr=2025-10-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792576883544, onlineIssueDateStr=2026-10-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792576883544, creator=13701087609, updateTime=1792576883544, updator=13701087609, issue=Issue{id=1319726331013742638, tenantId=1146029695717560320, journalId=1263187385517883426, year='2025', volume='53', issue='10', pageStart='2753', pageEnd='3078', issueExtLink='null', onlineDate='null', pubDate='1760889600000', pubDateStr='2025-10-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792576876643, creator='13701087609', updateTime=1792577470184, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319728820630962433, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726331013742638, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319728820635156738, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726331013742638, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2912, endPage=2928, ext={EN=ArticleExt(id=1319726361531502787, articleId=1319726359962833090, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Research Progress and Prospects on Artificial Intelligence in the Field of Flat Glass Production: Technological Integration and Industrial Transformation, columnId=1321945301351518491, journalTitle=Journal of the Chinese Ceramic Society, columnName=Special Issue on Technological Progress and Industrial Development of Advanced Glass Materials—Review, runingTitle=null, highlight=null, articleAbstract=

Flat glass as an important foundational material in various industries such as construction, automotive, and electronics has a significant importance in promoting the development of related industries through its production efficiency and product quality. With the development of the global economy and the continuous growth of demand for flat glass in various applications, the size of the global flat glass market is still expanding. However, the conventional flat glass industry faces some challenges in production, testing, logistics, and other aspects due to its low production efficiency, unstable product quality, high energy consumption, and high logistics costs. The rapid development of artificial intelligence technology brings some opportunities for transformation to the flat glass industry. It can play an important role in material and performance prediction, intelligent control of production processes, precise quality inspection, and efficient logistics management, effectively improving production efficiency, product quality, and market competitiveness, reducing production costs and energy consumption, and promoting the sustainable development of the industry.

This review mainly discusses the current development status and existing problems of artificial intelligence in the field of flat glass production, revealing the practical effects and challenges faced by artificial intelligence technology in promoting the intelligent development of the flat glass industry. In terms of component design and performance prediction, machine learning algorithms establish accurate prediction models via mining inherent patterns in a large amount of experimental data. For instance, the research and development cycle of new materials shortens and research and development costs reduce via combining neural networks and genetic algorithms to design new glass materials with specific optical properties and using random forest models to design components for lightweight and low expansion curtain wall glass. In terms of production process control, artificial intelligence technology plays an important role in core processes such as raw material processing, melting, forming, annealing, defect detection, cutting and sorting. Fuzzy modeling and genetic algorithm based on layered architecture are used for furnace operation optimization, and computer vision models are used for float glass melting furnace liquid level detection and control, thus improving production efficiency and product quality. In terms of identifying and diagnosing product defects, deep learning technology improves recognition accuracy and practicality through innovative model architecture, multimodal data fusion, and optimized computational efficiency. Object detection and segmentation models based on convolutional neural networks are widely used in glass defect detection. In terms of transportation, warehousing, and scheduling of flat glass raw materials, intelligent warehousing management systems and intelligent scheduling systems are gradually applied to flat glass enterprises, effectively improving the efficiency and accuracy of warehousing management and reducing transportation costs.

, authors=Shiqing XU, Tianlin CHEN, Linlin LIU, Shimin LIU, authorsList=Shiqing XU, Tianlin CHEN, Linlin LIU, Shimin LIU, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319726361921573060, articleId=1319726359962833090, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=人工智能在平板玻璃生产领域的研究进展与展望：技术融合与产业变革, columnId=1321945301657702684, journalTitle=硅酸盐学报, columnName=先进玻璃材料技术与产业发展专题—综合评述, runingTitle=null, highlight=null, articleAbstract=

在玻璃生产领域，随着深度学习、大数据分析等技术的不断发展，人工智能（AI）在提升生产效率和产品质量方面取得了显著的进展。虽然人工智能在平板玻璃领域展现出了巨大的潜力，但目前仍存在一些技术难题和应用瓶颈。本文主要从当前国内外平板玻璃生产领域人工智能的发展现状和存在的问题等方面进行综合论述，揭示人工智能技术在推动平板玻璃行业智能化发展过程中的实际效果和面临的挑战。此外，对人工智能在平板玻璃领域的未来发展进行了前瞻性的展望。

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