GOALI/Collaborative Research: Curating Complex Data Sets for Machine Learning Applied to Flexible Assembly Design and Optimization

GOALI/协作研究：为应用于灵活装配设计和优化的机器学习管理复杂的数据集

• 批准号: 2029905
• 负责人: Jami Shah
• 金额: $ 33.34万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029905&HistoricalAwards=false
• 关键词: GOALI; Collaborative; Research; Curating; Complex

In highly competitive markets, such as the automotive sector, product development time, quality, and cost are all critical. Successfully meeting such goals requires rapid exploration of design alternatives. The ultimate goal of this Grant Opportunity for Academic Liaison with Industry (GOALI) project is to advance data-driven design space exploration in automotive structural design using Artificial Intelligence. The development of Data Science to advance Machine Learning is highly dependent on the availability of large data sets that meet certain technical goals for algorithm training and validation. While training data sets are widely available for social networks, consumer preferences, and finance, such data sets need to be artificially curated for engineered products. This project will produce large data sets of alternative design configurations for particular engineering design objectives interrelated with technologically verified performance metrics. The research will focus on the application domain of flexible assembly design, a multi-stage design and manufacturing process widely used in the automotive and appliance industries. No specialized expertise will be needed for using the resulting deep learning tools once they have been trained and validated. In addition to advancing Data Science, another impact of this work will be democratization of complex structural design and analysis by supporting design and manufacturing decisions made by individuals without advanced degrees. It will also enable the next generation of engineers to be educated about applying advancing Machine Learning to engineering design and manufacturing and adapting data-driven tools in product development. This project will investigate data curation characteristics (e.g., volume, modality, granularity, heterogeneity, balance) while simultaneously considering the application domain and capabilities of the related Artificial Neural Net algorithms, including convolution, recurrent, generative adversarial networks, multi-layer perceptrons, and pooling architectures. To generate the required data sets, an automated simulation pipeline will be formulated that meets curation criteria. The results will be verified through industry benchmarks and experimental data from the industrial partner (Honda). Mathematical methods will be devised to extract key performance parameters from the simulation data. Additional methods will be designed to investigate abstractions, decompositions, and partitions of each data sample into sub-sets suitable for processing in parallel through federated Artificial Neural Nets, or individually through distributed machine learning networks, as chosen by the research community. All data sets will be published through Amazon Cloud for use by other engineering design researchers to advance design science in their respective fields.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.

在高度竞争激烈的市场中，例如汽车行业，产品开发时间，质量和成本都是至关重要的。成功实现此类目标需要快速探索设计替代方案。这种与工业联络（Goali）项目的学术联络机会的最终目标是使用人工智能推进汽车结构设计中的数据驱动设计空间探索。数据科学的发展以推进机器学习，高度取决于大型数据集的可用性，这些数据集符合算法培训和验证的某些技术目标。虽然培训数据集广泛用于社交网络，消费者的偏好和金融，但这些数据集需要为工程产品进行人工策划。该项目将为特定的工程设计目标提供与技术验证的性能指标相关的特定工程设计目标的大量数据集。该研究将重点放在灵活装配设计的应用领域，这是一种多阶段的设计和制造过程，广泛用于汽车和电器行业。一旦经过培训和验证，就不需要专业知识来使用所得深度学习工具。除了推进数据科学外，这项工作的另一个影响还将是通过支持没有高级学位的个人做出的设计和制造决策来对复杂的结构设计和分析进行民主化。这也将使下一代工程师能够在产品开发中将高级机器学习应用于工程设计和制造以及调整数据驱动的工具的教育。该项目将研究数据策划特征（例如，体积，模态，粒度，异质性，平衡），同时考虑相关人工神经净算法的应用领域和能力和合并体系结构。为了生成所需的数据集，将制定符合策展标准的自动模拟管道。结果将通过工业合作伙伴（本田）的行业基准和实验数据进行验证。将设计数学方法以从仿真数据中提取关键性能参数。将设计其他方法来研究每个数据样本的抽象，分解和分区中，适用于通过联合人工神经网并行处理，或通过研究社区选择的分布式机器学习网络单独处理。所有数据集将通过Amazon Cloud发布，以供其他工程设计研究人员在各自的领域中推进设计科学。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来支持的。 。