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在工业4.0的背景下,制造过程的预测性质量检查框架
Stefan Rydzi1, Barbora Zahradnikova2, Zuzana Sutova2
1Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology in Bratislava, 811 07 Bratislava, Slovakia.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究介绍了一种基于人工智能的软件框架,用于预测汽车质量,旨在减少缺陷和制造时间. 该系统增强了缺陷检测,并通过优化生产流程提供了显著的经济效益.
科学领域:
- 人工智能在制造业中的应用
- 汽车质量控制系统 汽车质量控制系统
- 机器学习用于预测性维护
背景情况:
- 目前的汽车质量控制依赖于耗时的手动检查和试驾.
- 到达客户的有缺陷的车辆导致成本增加,效率降低和声誉受损.
- 在汽车生产中,对先进的数据驱动解决方案的需求对于竞争优势至关重要.
研究的目的:
- 开发一种创新的AI驱动的软件框架,用于预测汽车质量.
- 整合机器学习,以便在生产线末端更好地检测缺陷.
- 为了减少制造时间,成本,并防止有缺陷的车辆到达消费者.
主要方法:
- 开发一种包含人工智能 (AI) 功能的新型软件框架.
- 使用机器学习 (ML) 技术进行预测性质量评估.
- 实施人工智能,以增强缺陷检测和个性化道路测试优化.
主要成果:
- 预测性质量检查框架在缺陷检测准确度方面取得了显著的改进.
- 该系统有效地支持个性化道路测试,优化资源配置.
- 预计每年至少节省20万分钟的生产时间,基于10%的试驾减少.
结论:
- 将人工智能集成到汽车质量控制中,为持续改进提供了可持续的长期解决方案.
- 人工智能框架提高了生产效率并缩短了制造时间表.
- 预测质量系统提供了巨大的经济效益,并加强了整体汽车制造工艺.
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