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

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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.