为制造业揭开数据和人工智能的神秘面纱:一家大型计算机制造商的案例研究

IF 3.2 Q1 Computer Science
Yi-Chun Chen, Bo-Huei He, Shih-Sung Lin, Jonathan Hans Soeseno, Daniel Stanley Tan, Trista Pei-chun Chen, Wei-Chao Chen
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引用次数: 1

摘要

在这篇文章中,我们讨论了一级电子制造设施中几个智能制造项目的背景和技术细节。我们使用历史数据和递归神经网络设计了一个管理电子零件物流预测和库存准备的流程,以实现对当前方法的显著改进。我们提出了一个通过计算机视觉和自动化技术自动鉴定笔记本电脑软件用于大规模生产的系统。其结果是一个可靠的系统,可以在资格认证过程中节省数百人年的时间。最后,我们创建了一种基于深度学习的产品外观视觉检测算法,与传统方法相比,该算法所需的缺陷训练数据要少得多。根据生产需要,我们设计了一台适合我们算法和工艺的自动光学检测机。我们还讨论了在工厂环境中进行数据收集和启用智能制造项目的问题,这些项目在工艺创新和成本节约措施之间保持着微妙的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demystifying data and AI for manufacturing: case studies from a major computer maker
In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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