Data Fusion Pipelines for Autonomous Smart Manufacturing

In smart manufacturing, data-driven models characterize variable relationships, which are used for decision making to achieve optimal operations. However, data-driven models may not be adequate when modeling assumptions are violated in manufacturing personalization. To advance smart manufacturing to future autonomous manufacturing, we propose data fusion pipelines as a combination of method options (i.e., data fusion or machine learning steps) for manufacturing process modeling. To avoid executing all pipelines, we associate the pipelines with a learning-to-rank method to rank the pipelines with Top-N prediction accuracy, where N is determined by the computation resources. This approach improves the ease of using data fusion and machine learning methods, and effectively avoids large computation workloads in executing of all data fusion pipelines. Case studies in thermal spray coating, aerosol® jet printing, and fused deposition modeling manufacturing are used to demonstrate the effectiveness and efficiency of the proposed approach. The proposed approach is scalable for a larger collection of method options, different manufacturing conditions, and various computation systems and networks.