Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Business | en_US |
| dc.contributor.advisor | 徐鑫 (MM) | en_US |
| dc.creator | 张伟 | - |
| dc.creator | Zhang, Wei | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14501 | - |
| dc.language | Chinese | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | 人工智能驱动的制造 AA 系统整体设备效能优化 | en_US |
| dcterms.abstract | 工业 4.0 时代的到来，通过引入人工智能（AI）、物联网（IoT）和大数据分析等先进技术，极大地推动了制造系统的流程监控和优化。本研究旨在探讨如何利用人工智能技术来影响制造系统中的性能效率和错误率。通过分析制造系统中的海量数据，人工智能算法可实现异常检测、预测性维护和流程优化，从而全面提升设备综合效率。为验证人工智能算法的潜力，本研究计划在多个工业制造系统中进行现场实验。我们将使用差分法（DID）来量化人工智能驱动的优化与传统非人工智能 OEE 管理系统相比的有效性。研究设计将包括对比实施人工智能算法的生产线和继续使用传统方法的生产线，通过定量分析来评估人工智能对制造系统效能的影响。 | en_US |
| dcterms.abstract | 本研究使用盛泰光电科技股份有限公司 2023-2025 分小时的内部数据，研究了实施人工智能算法的生产线和继续使用传统方法的生产线对性能效率和错误率的影响。研究发现，（1）人工智能驱动的优化可提高性能效率；（2）人工智能驱动的优化能提高错误率；（3）复杂程度负向调节 ai 驱动的优化对性能效率的提高；（4）复杂程度正向调节 ai 驱动的优化对错误率的提高。 | en_US |
| dcterms.abstract | 本研究的预期成果将为人工智能在制造系统优化中的应用提供实证支持，并强调其在实际生产环境中的巨大潜力。通过数据驱动的实时分析和决策，人工智能技术有望帮助制造企业识别并消除低效源，减少浪费，提高整体生产率。最终，本研究将为制造业实现更高水平的运营绩效提供理论和实践基础。 | en_US |
| dcterms.alternative | AI-driven manufacturing AA system for optimizing overall equipment efficiency | en_US |
| dcterms.extent | 1 volume (various pagings) : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | D.Mgt. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 9066.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.11 MB | Adobe PDF | View/Open |
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