中空玻璃生产线的数字孪生和预测性质量解决方案

IF 5.9 2区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Manufacturing Pub Date : 2024-05-27 DOI:10.1007/s10845-024-02426-y

Gülcan Aydin, Mehmet Tezcan, Bayram Ozgen, Tuğçe Nur Özkan

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引用次数: 0

摘要

本研究是一项国际研发计划的重要组成部分，该计划旨在调查数字双胞胎和预测性质量解决方案在中空玻璃制造业中的应用，以加强质量控制和简化生产流程。影响中空玻璃转化为优质节能产品的关键因素是气体填充率。因此，本研究重点关注气体填充过程的实时监控和分析。同时，实施预测性质量解决方案，以提高产品质量，减少缺陷。因此，这些技术在提高中空玻璃生产质量和促进国际范围内的可持续生产实践方面显然具有巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Digital twin and predictive quality solution for insulated glass line

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Digital twin and predictive quality solution for insulated glass line

This study is an integral part of an international research and development initiative investigating the application of digital twins and predictive quality solutions to enhance quality control and streamline production processes within the insulating glass manufacturing industry. The critical factor influencing the transformation of insulating glass into a high-quality, energy-efficient product is the gas filling rate. Therefore, this study focuses on the real-time monitoring and analysis of the gas filling process. Concurrently, predictive quality solutions are implemented to improve product quality and reduce defects. Consequently, it is evident that these technologies hold significant potential to advance the quality of insulating glass production and promote sustainable production practices on an international scale.

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来源期刊

Journal of Intelligent Manufacturing 工程技术-工程：制造

CiteScore

19.30

自引率

9.60%

发文量

171

审稿时长

5.2 months

期刊介绍： The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.