SBIR Phase I: Multivariate Shrinkage Sensor For Injection Molding

SBIR 第一阶段：用于注塑成型的多元收缩传感器

• 批准号: 1843921
• 负责人: Rahul Panchal
• 金额: $ 22.5万
• 依托单位: LEONINE TECHNOLOGIES INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843921&HistoricalAwards=false
• 关键词: SBIR; Phase; Multivariate; Shrinkage; Sensor

The broader impact/commercial potential of this project is that it addresses the injection molding industry which generates over $100 billion in revenue in the US alone. The proposed multivariate shrinkage sensor promises a leap forward from the current use of in-mold pressure/temperature sensors for indirect estimation of molded part dimensions and quality. Direct measurement of the shrinkage along with pressure and temperature in situ from the proposed sensor will enable improved process and quality control methods for injection molding which will eliminate the burden of frequent post-molding inspections and allow fully automated manufacturing. As a result, over 30 % of manufacturing cost and time will be saved while achieving 100% quality assurance even for the parts with tighter specifications. The proposed sensor will help heavily regulated industries such as medical devices and automobile manufacturing in achieving significant time reduction for process set-up and stabilization, and improvement in parts quality while meeting stringent regulatory requirements. Each percentage point improvement in productivity due to cycle time reductions, yield improvements, and related automation corresponds to cost savings of more than $1 billion per year in the United States alone while reducing the energy consumption and environmental waste related to molding, discarding, and recycling of defective plastic parts.This Small Business Innovation Research Phase I project provides a creative and original concept: a multivariate design for measuring shrinkage along with pressure and temperatures via a conventionally packaged device. Furthermore, knowledge and understanding are expected to be advanced in the development and validation of improved on-line models for estimating molded part shrinkage in real-time. Previous research did not have access to in-mold shrinkage data and so propagated faulty initial conditions throughout the shrinkage analysis. Due to the lack of direct observability and controllability of the molded part dimensions, most molders rely on the use of cavity pressure traces or part weight measurements as estimators of the part dimensions. However, there still is no technology or research available to monitor and predict the shrinkage on-line during production to improve, to optimize and to control parts quality and processing parameters without using expensive instrumentation. The outcome of this Phase I project will fill this gap and develop a fully functional multivariate shrinkage sensor which will enable measurement of molded product dimensions in situ prior to ejection from the mold.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.

该项目更广泛的影响/商业潜力在于，它解决了仅在美国就产生超过1000亿美元收入的注塑成型行业。拟议的多元收缩传感器承诺从目前使用的模内压力/温度传感器间接估计成型零件的尺寸和质量的飞跃。从所提出的传感器直接测量收缩率沿着以及压力和温度，将能够改进注塑成型的工艺和质量控制方法，这将消除频繁的成型后检查的负担，并允许完全自动化的制造。因此，即使是规格更严格的零件，也可以节省30%以上的制造成本和时间，同时实现100%的质量保证。该传感器将帮助医疗设备和汽车制造等受到严格监管的行业大幅缩短流程设置和稳定的时间，并在满足严格监管要求的同时提高零件质量。由于周期时间缩短、产量提高和相关自动化，生产率每提高一个百分点，仅在美国每年就可以节省超过10亿美元的成本，同时减少与成型、丢弃和回收有缺陷塑料部件相关的能源消耗和环境浪费。这个小型企业创新研究第一阶段项目提供了一个创造性的原创概念：一种多变量设计，用于通过常规封装的装置测量收缩率沿着压力和温度变化。此外，知识和理解，预计将在开发和验证的改进的在线模型，估计模塑件收缩率在实时先进。以前的研究没有获得模内收缩数据，因此在整个收缩分析中传播了错误的初始条件。由于缺乏直接的可观察性和可控性的成型零件的尺寸，大多数模具依赖于使用腔压力的痕迹或部分重量测量作为估计的部分尺寸。然而，仍然没有技术或研究可用于在生产期间在线监测和预测收缩，以在不使用昂贵的仪器的情况下改进、优化和控制零件质量和加工参数。第一阶段项目的成果将填补这一空白，并开发出一种功能齐全的多元收缩传感器，该传感器将能够在脱模前现场测量成型产品的尺寸。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。