SBIR PHASE I: Manufacturing Workforce - Novice to Expert - Training Program

这个小型企业创新研究第一阶段项目旨在制作一个原型制造劳动力培训课程，增强的软件程序，以及专门针对复合材料设计和产品生命周期管理（PLM）的方法，包括更有效地使用Catia V5（一套集成的计算机辅助设计（CAD），计算机辅助工程（CAE），以及用于数字产品定义和仿真的计算机辅助制造（CAM）应用程序），通过提高其可视化能力，在波音7 E7生产劳动力的装配中发挥作用。 知识渊博、足智多谋的生产劳动力，不仅在航空航天业，而且在其他制造业，对于满足全球市场的竞争要求至关重要。 在复杂的硬件、软件和制造工艺的可用性与必须利用它们的劳动力的技能和能力之间存在差距。 因此，本计画将以最先进的3d全像技术作为知识传递的工具，以实证的方式来证明“认知学习”的价值。 研究设计的特点是两个劳动力培训队列的基础上随机分配到控制组和实验组。 控制组接受传统训练，实验组接受技术驱动的“认知学习”模式课程。 能力评估，除了受训者前和后的知识评估，将表明，实验组将进步从“新手”工人到“专家”工人在一个显着更短和更有效的培训过程中，通过使用“认知学习”模式。 目前和未来的工程师和技术人员在航空航天，汽车，消费品，电子，重型设备，生物医学设备和机床，将需要有各种材料和复合材料和新的制造工艺的理解水平。为了具有竞争力，他们必须获得先进制造和材料教育和培训的组合，以帮助使用更便宜的工艺生产高度耐用的产品，同时提高效率。 这正是以科学为基础、技术驱动的认知学习”制造业劳动力培训模型原型在经过实地测试和经验记录后所能做到的。 只要证明原型运行良好，然后扩展到下一阶段的研究和开发，以充分验证和扩展模型，将打开巨大的商业化机会的大门。 总之，制造业劳动力培训将从传统的教育结构转向以学生为中心的学习环境，为先进制造业提供虚拟团队，三维可视化，批判性思维和现实生活场景以及解决问题的能力。

This Small Business Innovation Research Phase I project is designed to produce a prototype manufacturing workforce training curriculum, enhanced software programs, and methodology that specifically address Composite Design and Product Lifecycle Management (PLM), including more effective use of Catia V5 (an integrated suite of Computer Aided Design (CAD), Computer Aided Engineering (CAE), and Computer Aided Manufacturing (CAM) applications for digital product definition and simulation), by increasing its visualization capabilities, in the assembly of the Boeing 7E7 production workforce. A knowledgeable and resourceful production workforce, not only in aerospace, but also in other manufacturing industries, is essential in order to meet the competitive requirements of a global market. There is a gap between the availability of sophisticated hardware, software, and manufacturing processes and the skills and competence of the workforce who must utilize them. This project, therefore, proposes to empirically demonstrate the value of "cognitive learning", using state-of-the-art 3d holographic technology as a knowledge transfer tool. The research design features two workforce-training cohorts based on a random assignment to control and experimental groups. The control group will receive traditional training and the experimental group will receive the technology-driven "cognitive learning" model curriculum. Competency-based assessments, in addition to trainee pre- and post-knowledge assessments, will demonstrate that the experimental group will progress from "novice" workers to "expert" workers in a dramatically shorter and more effective training process through the use of the "cognitive learning" model. Current and future engineers and technicians in aerospace, automotive, consumer goods, electronics, heavy equipment, biomedical devices and machine tools, will be required to have a level of understanding of a variety of materials and composites and new manufacturing processes. To be competitive, they must have access to a combination of advanced manufacturing and materials education and training to help produce highly-durable goods using less expensive processes while increasing efficiency. That is precisely what the scientifically-based, technology-driven Cognitive Learning" manufacturing workforce training model prototype, when field-tested and documented empirically, will do. Just demonstrating that the prototype works well, and then expanding into the next phase of the research and development to fully verify and expand the model, will open the door to tremendous commercialization opportunities. In summary, manufacturing workforce training will move from the traditional structure of education to a student-focused learning environment that provides for virtual teaming, three-dimensional visualization, critical thinking and real-life scenarios and problem solving for advanced manufacturing.