Artificial intelligence monitoring of direct strip casting

薄带直接连铸的人工智能监控

• 批准号: 571654-2021
• 负责人: Daymond, MarkMR
• 金额: $ 1.68万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759797
• 关键词: Artificial; intelligence; monitoring; direct; strip

Identifying defective products on a production line is a labour-intensive and costly process, resulting in long product hold times and/or lost time and effort. Quality-related costs are a significant economic and environmental burden: even a slight variation in production processes or materials (many invisible to the human eye) can result in an entirely defective production run. Conventional machine vision-based surface inspection methods work well for surface inspection when defects have strong edges/contrast, but they are greatly affected by noise and lighting conditions. This proposal will address detection of defects in a novel aluminum strip-casting facility. To better handle noise and lighting issues, rather than conventional image processing, this proposal will develop state-of-the-art machine learning approaches, coupled with industrial-ready visual capture technologies. Initially focused on postmortem assessment of material, the proposal will also work to develop an on-line defect detection methodology. With suitable development, the final goal will be not just detect defects but help guide optimisation of the strip casting process for given aluminum alloy. The proposal brings together two small but innovative industrial contributors focused on applied uses of machine learning and novel aluminum casting technologies, respectively. While the machine learning approach will be applied here to industrial strip-casting of aluminum, we consider this a development test-bed for procedures and processes that are directly transferable to a range of continuous production industries. To enable application across a wide range of industries, the approach developed will therefore be modular and flexible. Our goal is to address a significant knowledge gap with the potential to benefit a wide range of Canadian industries.

在生产线上识别有缺陷的产品是一个劳动密集型和昂贵的过程，导致长的产品保持时间和/或时间和精力的损失。与质量相关的成本是一个重大的经济和环境负担：即使生产过程或材料（许多是人眼不可见的）的微小变化也可能导致完全有缺陷的生产运行。 当缺陷具有强边缘/对比度时，传统的基于机器视觉的表面检测方法对于表面检测工作良好，但它们受到噪声和照明条件的极大影响。该建议将解决在一个新的铝带铸造设施的缺陷检测。 为了更好地处理噪声和照明问题，而不是传统的图像处理，该提案将开发最先进的机器学习方法，以及工业级的视觉捕捉技术。 最初侧重于材料的事后评估，该提案还将致力于开发在线缺陷检测方法。 通过适当的开发，最终目标将不仅仅是检测缺陷，而是帮助指导特定铝合金的带材铸造工艺的优化。该提案汇集了两个小型但创新的工业贡献者，分别专注于机器学习和新型铝铸造技术的应用。 虽然机器学习方法将在这里应用于工业铝带铸造，但我们认为这是一个可直接转移到一系列连续生产行业的程序和工艺的开发测试平台。 为了使其能够在广泛的行业中应用，因此开发的方法将是模块化和灵活的。 我们的目标是解决一个重大的知识差距，有可能使加拿大各行各业受益。