DeFacto · Design Automation for Smart Factories

Design Automation for Smart Factories

Manufacturing is undergoing what many identified as "a fourth industrial revolution": production lines are becoming complex networked systems, equipped with teams of robots that manipulate products and take care of the logistics and shop-floor organization, sensors gathering large amounts of data, and computing platforms providing situational awareness and intelligent control. "Smart" factories will turn into complex, heterogeneous cyber-physical systems (CPSs), a transformation that will offer unprecedented opportunities. Reconfigurable production lines will be able to face the increasing demand for customized products and move from mass production to mass customization, data analytics will enable optimizing production costs, supply and logistics chains, and integrating different aspects of the value production chain, from the sales office to the production line. Performing predictive maintenance at scale and reducing production downtime will be possible. Indeed, exploiting the opportunities provided by such innovations will be crucial to increasing the added value and productivity of European manufacturing and addressing the challenges faced by the European economy and society. These opportunities will come, however, with a series of engineering challenges. A smart manufacturing system must integrate a diverse set of components while offering strong guarantees in terms of functionality, reliability, safety, and cost. This heterogeneity in components and system requirements inevitably calls for models, specification formalisms, and design constraints of different nature to represent a design space that is difficult to extensively explore in a reasonable time, an issue that is often recognized as the "explosion in complexity" of today's industrial CPS design. Mirroring the success of electronic design automation (EDA) in taming the complexity of microchip design in the '80s, system design automation is expected to play a crucial role in reducing the complexity of CPS design. The scientific goal of DeFacto is to advance the state of the art in system design automation by developing novel modeling paradigms, scalable algorithms, and tools to aid the design of smart manufacturing systems. DeFacto aims at automatically defining the architecture (i.e., the system components and their interconnections) and synthesizing the control software for the industrial CPSs that are part of smart manufacturing systems, ultimately fostering their widespread adoption. The methodologies developed in DeFacto reason about systems using reliable compositional abstractions of system behaviors based on assume-guarantee (A/G) contracts. Intuitively, an A/G contract represents the interface of a component as a pair of assumptions and guarantees. Assumptions are the behaviors that a component expects from the environment; guarantees are the behaviors the component promises in the context of the assumptions. Contracts are then mathematical models that provide rigorous composition rules and mechanisms to analyze complex system behaviors, validate the design requirements, and develop system components in a modular and hierarchical way. The project has three main research objectives: - Objective 1: Identify requirements and architectures for the design of industrial CPSs and define contract-based representations for the requirements and the system components at different abstraction levels. - Objective 2: Develop a formal methodology, algorithms, and computational tools for contract-based requirement validation, design-space exploration, and model refinement. The methodology refines the system-level requirements and maps them to a system architecture and a set of control algorithms. - Objective 3: Develop synthesis and mapping algorithms to generate software implementations from higher-level models of the system architecture and the control algorithms. The implementations include monitors detecting undesired behaviors due to uncertainty in sensors, communication networks, and machine learning components.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз