SBIR Phase I: Extrusion quality inspection with machine learning

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响将是提高连续挤压环境中产生的产品的产品质量。 例如，2-55%的原材料可能在塑料挤出中被浪费。仅在美国，每年就有至少5亿美元的原材料损失，造成了额外的环境问题，因为这种废塑料通常不能重复使用或回收。人工检查在规模上对于该过程是有问题的。 该项目将应用智能系统自动检测缺陷并采取行动，提高效率和财务绩效。该系统最初将应用于塑料挤出，随后将应用于包括金属、食品和制药生产在内的广泛行业。这个小型企业创新研究（SBIR）第一阶段项目将允许开发新的机器学习和人工智能技术，以自动检测连续制造环境中质量不合格的产品。该研究将从一系列传感器输入中生成真实世界的塑料生产数据，以训练AI模型对输出进行分类。AI/ML中的新方法将被应用于开发健壮的、适应性强的模型，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故障产品输出的技术开发，例如相机镜头的污垢或硬件的意外移动。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.