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

在竞争激烈的市场中，如汽车行业，产品开发时间、质量和成本都至关重要。要想成功实现这些目标，就需要迅速探索设计替代方案。GOALI项目的最终目标是利用人工智能在汽车结构设计中推进数据驱动的设计空间探索。数据科学促进机器学习的发展高度依赖于满足某些算法训练和验证技术目标的大型数据集的可用性。虽然训练数据集广泛适用于社交网络、消费者偏好和金融，但此类数据集需要为工程产品进行人工管理。该项目将为与技术验证的性能指标相关的特定工程设计目标产生替代设计配置的大型数据集。这项研究将集中在柔性装配设计的应用领域，这是一种广泛应用于汽车和家电行业的多阶段设计和制造流程。所产生的深度学习工具一旦经过培训和验证，将不需要专门知识即可使用。除了推进数据科学，这项工作的另一个影响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实现复杂结构设计和分析的民主化。它还将使下一代工程师能够接受有关将先进的机器学习应用于工程设计和制造以及在产品开发中采用数据驱动工具的培训。该项目将研究数据管理特征(例如，数据量、形态、粒度、异质性、平衡性)，同时考虑相关人工神经网络算法的应用领域和能力，包括卷积、递归、生成性对抗网络、多层感知器和池结构。为了生成所需的数据集，将制定一条满足管理标准的自动化模拟管道。结果将通过行业基准和工业合作伙伴(本田)的实验数据进行验证。将设计数学方法从模拟数据中提取关键性能参数。将设计其他方法来调查每个数据样本的抽象、分解和划分成适合通过联合人工神经网络并行处理的子集，或根据研究界的选择通过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，供其他工程设计研究人员使用，以促进各自领域的设计科学。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.