EAGER/Cybermanufacturing: Data-driven Real-time Embodiment of Advanced Manufacturing Systems (DREAMS)

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Advanced manufacturing systems, cyber-enabled tools equipped with intelligent sensors and communication abilities, have become increasingly capable, ubiquitous, and standardized to increase the productivity, quality, and complexity of manufactured goods. Until the recent introduction of MTConnect, a standardized protocol for extracting data from a system's sensors, this sensor data had only been used by the machine itself for control purposes - resulting in a lost opportunity to analyze a wealth of in-process measurements for optimization, quality, process planning, and other improvements that could result in more resource, energy, and cost efficient operation. The primary goal of this EArly-concept Grant for Exploratory Research (EAGER) project is to design and implement a scalable Data-driven Real-time Embodiment of Advanced Manufacturing Systems (DREAMS) platform to interface with MTConnect in order to aggregate, analyze, and provide new opportunities to interact with process data from advanced manufacturing systems. DREAMS will be established in collaboration with cybersecurity researchers in order to build it with security and resilience as a requirement, rather than an afterthought, and the design will address research questions inherent to providing distributed access and computational abilities with facility-level security. DREAMS will enable secure, real-time processing and conditioning of sensor and process data from advanced manufacturing system controllers, via the MTConnect interoperability protocol for computer numerically controlled (CNC) machine tools. Once the DREAMS architecture is in place, a handful of pilot research domains will be explored, including: research into how applications and auxiliary systems guided by DREAMS could influence manufacturing education; enhancing real-time diagnostics and prognostics with continuously-updated and adaptive physical simulations; and how DREAMS could inform intelligent process parameter and toolpath selection for energy and resource efficiency. Success in this initial project would lay the foundation for a wealth of follow-on work leveraging and enhancing the DREAMS platform, for example, in complex event processing, real-time data analysis, and forecasting of complex scenarios originating in workplaces and with future advanced manufacturing processes. DREAMS can facilitate more intelligent, precise, resource-efficient, resilient, and connected manufacturing services, and given that it will be built around the MTConnect protocol, it will be low-cost and available to universities, large corporations, SMEs, and entrepreneurs alike.