Investigating the Effectiveness of Machine Learning Paradigms for Supporting Engineering Designers in Rapidly Evolving Digital Manufacturing

制造技术正以前所未有的速度发展，因为它变得越来越数字化。 利用新的制造技术对于维护国家安全和繁荣至关重要，但即使是专门的专家和领先的公司也很难跟上制造业的快速发展。 这使得工程师很难了解最新的制造技术，并设计出充分利用新制造工艺的产品。 幸运的是，许多新的制造技术依赖于数字模型，这些数字模型可以产生丰富的设计数据，机器学习可以应用于这些数据来获得设计知识。 虽然设计和制造数据集可能是有用的，但它们在解决方案的数量和质量方面也存在很大差异。 这项工作调查了这些数据集的大小和质量如何与机器学习见解的准确性和有用性相关，以及这如何影响为工程设计师提供的支持。 在这项工作中，我们专注于增材制造（也称为“3D打印”）作为一种代表性的数字制造技术，正在迅速发展和增长，预计将为国家做出重大贡献。未来的制造业组合。作为这项工作的一部分，对工程专业学生进行的研究将用于提供技能培训和收集数据，帮助他们为制造业劳动力做好准备。该研究将结合联合收割机机器学习、增材制造和可解释的人工智能，评估来自现有众包增材制造设计挑战的自动设计反馈的使用情况。 首先，零件设计将从开放的在线存储库中挖掘，以及通过在课堂设计挑战中建立的策展存储库。 接下来，将实施机器学习管道，从策划的数字存储库中提取设计模式。 这将使得测试存储库大小对设计反馈的准确性以及存储库大小对反馈粒度的影响成为可能。 最后，将进行用户验证研究，学生将承担特定于增材制造技术的设计任务。 通过扩展先前开发的具有可解释功能的机器学习管道，将向一些参与者提供具有不同特征的反馈。 具体的技术交付成果将包括（1）一个新的体素化零件设计数据集，（2）更深入地了解存储库大小和质量对机器生成反馈有用性的影响，和（3）经验证据的影响，真实的-该奖项反映了NSF的法定使命，并被认为是值得支持的，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。

Manufacturing technology is advancing at an unprecedented rate as it become increasingly digital. Taking advantage of new manufacturing technology is critical for maintaining national security and prosperity, but even dedicated experts and leading-edge companies struggle to keep pace with manufacturing's rapid advancement. This makes it difficult for engineers to learn about the latest manufacturing technology and design products that take full advantage of new fabrication processes as they become available. Fortunately, many new manufacturing technologies rely on digital models that produce abundant design data to which machine learning can be applied to derive design knowledge. While design and manufacturing datasets may be useful for that reason, they are also highly variable in both number and quality of solutions. This work investigates how the size and quality of these datasets relate to the accuracy and usefulness of machine learning insights, and how this impacts the support provided to engineering designers. In this work, we focus on additive manufacturing (also called "3D printing") as a representative digital manufacturing technology that is rapidly evolving and growing, and which is projected to contribute substantially to the nation?s future manufacturing portfolio. Studies conducted with engineering students as part of this work will be used to provide skill training as well as collect data, helping prepare them for the manufacturing workforce.The research will combine machine learning, additive manufacturing, and explainable artificial intelligence to evaluate the use of automated design feedback derived from existing crowdsourced additive manufacturing design challenges. First, part designs will be mined from open, online repositories as well as through curated repositories established in this work via in-class design challenges. Next, a machine learning pipeline will be implemented to extract design patterns from curated digital repositories. This will make it possible to test the effect of repository size on the accuracy of design feedback and of repository size on the granularity of feedback. Finally, a user validation study will be conducted in which students will undertake a design task specific to additive manufacturing technology. Feedback with varying characteristics will be provided to some participants by extending the machine learning pipeline developed previously with explainable capabilities. Specific technical deliverables will include (1) a novel dataset of voxelized part designs, (2) a deeper understanding of the impact of repository size and quality on usefulness of machine-generated feedback, and (3) empirical evidence of the impact of real-time additive manufacturing feedback on the solutions generated by engineering designers.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.