基于工业软件的电子信息行业智能制造过程优化措施

Optimization Measures for Intelligent Manufacturing Processes in the Electronic Information Industry Based on Industrial Software

在大规模、重复的工业生产中,采用计算机辅助的计算机辅助检测技术,可以极大地提升其工作效率并使其实现自动化。本文从电子信息智能制造产业需求出发,设计了一种实现产品质量检测分类的Resnet检测软件。通过实验数据验证分析,该模型在正常电子元件图像数据集中的分类识别率为97.84%,在缺陷电子元件图像数据集中的识别率为91.88%,均高于其他算法模型,表明研究构建的电子信息产品智能制造Resnet检测软件具备优越的元件检测性能,具备一定的实际应用价值。

In large-scale and repetitive industrial production,the use of computer-aided detection technology can greatly improve its work efficiency and achieve automation.This paper starts from the needs of the electronic information intelligent manufacturing industry,the article designs a Resnet detection software that implements product quality detection and classification.Through experimental data validation and analysis,the classification recognition rate of this model in the normal electronic component image dataset is 97.84%,and the recognition rate in the defect electronic component image dataset is 91.88%,which is higher than other algorithm models.This indicates that the research and construction of the electronic information product intelligent manufacturing Resnet detection software has superior component detection performance and practical application value.