GOALI: Modeling and Control for Manufacturing Intelligence with Cloud Computing and Storage

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

This Grant Opportunity for Academic Liaison with Industry (GOALI) project will investigate using a paradigm shift in manufacturing systems operation using a cloud-based framework to achieve higher quality, higher throughput, and lower energy usage. An emerging industry trend is to store the large amount of data produced on manufacturing plant floors in the cloud. This data includes production targets, quality inspection results, machine faults, etc, but has the potential to also include detailed energy usage information of each machine on the plant floor as well as maintenance logs of every machine. In current practice, this data is collected into dashboards that engineers can use to understand the current state of the manufacturing system. Within this project, we will use this data to automatically find relationships that exist between quality, throughput, maintenance, and energy usage. These relationships can be analyzed and leveraged to develop better production schedules, reducing the overall cost of production. To accomplish this goal, modular hybrid models of the different components in the manufacturing system will be developed. These hybrid models will include both the discrete-event behavior of the components as well as their continuous-variable dynamics. Leveraging the operational data produced on plant floors and pushed into cloud storage, automatic methods will be developed to extract the parameters for these models from real-time data streams. Finally, optimization functions will be developed that can encode the tradeoffs between productivity, energy usage, and maintenance, and the developed models will be used to solve for the optimal production parameters. This research will transform the state of the art of manufacturing operations, enabling a high degree of maintenance scheduling and customization to meet the ever-increasing demands of manufacturing operators. The generated knowledge on hybrid modeling and multi-objective system optimization for manufacturing systems will lead to a new paradigm of intelligent manufacturing through effective and efficient monitoring and control with the cloud. Furthermore, this research will lay the foundation towards a completely automated cloud-based manufacturing operation.