SBIR Phase I: Extrusion quality inspection with machine learning

SBIR 第一阶段：利用机器学习进行挤出质量检测

• 批准号: 2025977
• 负责人: Richard Marshall
• 金额: $ 25.6万
• 依托单位: COMPLETIONAI LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025977&HistoricalAwards=false
• 关键词: SBIR; Phase; Extrusion; quality; inspection

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to improve product quality in products generated in continuous extrusion environments. For instance, 2-55% of raw material can be wasted in plastics extrusion. In aggregate at least $500M raw material is lost each year in the US alone, creating additional environmental concerns because this waste plastic is typically not reusable nor recyclable. Manual inspection is problematic for this process at scale. This project will apply intelligent systems to automatically detect and act upon imperfections, improving efficiency and financial performance. The system will initially be applied to plastics extrusion, and later to a wide range of industries including metals, food and pharmaceutical production. This Small Business Innovation Research (SBIR) Phase I project will allow development of novel machine learning and artificial intelligence technologies to automatically detect output of substandard quality in continuous manufacturing environments. The research will generate real-world plastics production data from a range of sensor inputs to train AI models to classify outputs. New approaches in AI/ML will be applied to develop robust, adaptable models to infer error states in product output. Research will also cover the development of technologies to detect failed product output in changing factory conditions, such as fouling of camera lenses or unexpected movement of the hardware.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响是提高连续挤压环境中产生的产品的产品质量。 例如，塑料挤出中可以浪费2-55％的原料。每年仅在美国，每年至少损失了至少5亿美元的原材料，这引起了其他环境问题，因为这种废塑料通常不可重复使用或可回收。对于此过程，手动检查是有问题的。 该项目将应用智能系统自动检测并在不完美的情况下采取行动，从而提高效率和财务绩效。该系统最初将用于塑料挤出，后来又用于包括金属，食品和药品生产在内的各种行业。这项小型企业创新研究（SBIR）I期项目将允许开发新颖的机器学习和人工智能技术，可以在连续制造环境中自动检测不合格质量的输出。该研究将从一系列传感器输入中生成现实世界中的塑料生产数据，以训练AI模型以对输出进行分类。 AI/ML中的新方法将应用于开发可靠的，适应性的模型，以推断产品输出中的错误状态。研究还将涵盖在不断变化的工厂条件下检测产品产量失败的技术的开发，例如摄像机镜头的结垢或硬件的意外运动。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响来通过评估来进行评估。