SBIR Phase II: Simplifying the use of recycled plastics in film extrusion

这个小企业创新研究（SBIR）第二阶段项目的更广泛影响将是允许再生塑料比目前更有效和更经济地使用。监管和社会压力迫使人们重新考虑一次性塑料，塑料薄膜制造商必须大量使用再生塑料。然而，由于一次性塑料的低质量和不可预测的含量，制造商很难负担得起。通过使用拟议技术增加可用塑料的产量，预计将减少浪费，有可能每年在美国和加拿大减少650万吨二氧化碳排放量，在全球减少2800万吨二氧化碳排放量。此外，在制造业中更多地使用人工智能对美国具有战略优势，拟议的技术也适用于金属，纸张或先进材料。此外，技能短缺正在影响制造业，并可能因劳动力迅速老龄化而恶化。随着一代经验丰富的操作员退休，大量在职专业知识将在未来几年内流失。该解决方案可以缓解这一转变，作为专家决策系统推进智能化，帮助保持美国制造业的竞争力。这个小企业创新研究（SBIR）第二阶段项目将把人工智能（AI）能力和过程控制方法应用于塑料薄膜挤出，并随后应用于其他类型的制造。目前，制造商有硬件解决方案，尽管它们可能很昂贵，难以使用和维护，并且可能需要专业技能才能使用。相比之下，所提出的技术是一种基于软件的方法来控制复杂的塑料薄膜挤出过程，特别是在广泛可变的输入材料（如回收塑料）的背景下。人工智能软件将对生产环境的变化具有鲁棒性，并将随着时间的推移考虑过程漂移。这些技术能力在工业上是新颖的，在学术文献中是未知的。第一阶段的结果表明，该技术可以自动控制挤出过程，以实现最佳的稳态生产速度比目前通过人工控制的速度更快。基于人工智能的专家系统有效地重新创建了经验丰富的工厂操作员所默认的知识。这种类型的工业自动化有可能为更广泛的制造业创造价值。这项技术可能适用于更广泛的挤压制造工艺，如金属、纸张或先进材料的挤压。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 II project will be to allow recycled plastics to be used more efficiently and affordably than is currently possible. Regulatory and societal pressures are forcing reconsideration of single use plastics, and manufacturers of plastic film must use recycled plastic at higher quantities. However, it is difficult for the manufacturers to affordably reincorporate single-use plastics due to the low quality and unpredictable content of the material. Increasing yield of usable plastics through use of the proposed technology is expected to reduce waste, offering the potential to annually save 6.5 million metric tons of carbon dioxide emissions in the US and Canada, and 28 million metric tons globally. Also, the greater use of artificial intelligence in manufacturing is of strategic advantage to the US, with the proposed technology also applicable to metals, paper, or advanced materials. Furthermore, skills shortages are impacting manufacturing and are likely to worsen due to a rapidly aging workforce. A great deal of on-the-job expertise will be lost in the coming years as a generation of experienced operators retires. The proposed solution can ease this transition, acting as an expert decision system to carry the intelligence forward and help maintain US manufacturing competitiveness.This Small Business Innovation Research (SBIR) Phase II project will apply artificial intelligence (AI) capabilities and process control methods to plastic film extrusion, and subsequently to other types of manufacturing. Currently hardware solutions exist for manufacturers, though they can be expensive, difficult to use and maintain, and can require specialized skills to use. By contrast, the proposed technology is a software-based approach to the control of complex plastic film extrusion processes, particularly in the context of widely variable input materials such as recycled plastics. The AI software will be robust to changes in the production environment and will account for process drift over time. These technology capabilities are industrially novel and not known in the academic literature. Phase I outcomes suggest that the technology can automatically control extrusion processes to achieve optimal steady state production faster than is the currently possible via human control. The AI-based expert system effectively recreates the knowledge tacitly held by long-experienced factory operators. This type of industrial automation has the potential to be value-generating for the wider manufacturing sector. The proposed technology may be applicable to a wider range of extrusion manufacturing processes, such as extrusion of metals, paper or advanced materials.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.