Research on quality control and optimization of industrial process based on deep learning
基于深度学习的工业过程质量控制与优化研究

摘要

工业生产迈向智能化之际，质量控制与优化需求迫切。本研究运用深度学习技术，深度剖析工业流程数据。借助其精准的模式识别能力，构建模型监测生产异常、预测质量走势并优化工艺。从海量数据中提取关键特征，实现次品的提前预警，动态调控生产参数，有效提升产品质量，降低成本，为工业升级筑牢根基，增强企业市场竞争力。

Abstract

As industrial production moves towards intelligence, the need for quality control and optimization is urgent. This study employs deep learning technology to deeply analyze industrial process data. Leveraging its precise pattern recognition capabilities, it constructs models to monitor production anomalies, predict quality trends, and optimize processes. By extracting key features from massive datasets, it achieves early warnings of defective products, dynamically adjusts production parameters, effectively improves product quality, reduces costs, and lays a solid foundation for industrial upgrading, enhancing corporate market competitiveness.