Descripción del proyecto
Una tecnología rupturista para optimizar los sistemas de producción
Para lograr que la producción manufacturera siga siendo competitiva, los directores de producción tienen que diseñar y administrar complejos sistemas de producción colaborativos y reconfigurables que saquen el máximo partido a las tecnologías nuevas. Con todo, esto requiere aún muchos estudios para aprovechar al máximo los beneficios de las herramientas digitales para la fabricación. El objetivo del proyecto financiado con fondos europeos ASSISTANT es diseñar tecnologías rupturistas para la industria manufacturera mediante el empleo de la inteligencia artificial en pos de optimizar los sistemas de producción. La creación de gemelos digitales inteligentes constituye una de las piedras angulares de ASSISTANT. ASSISTANT combinará el aprendizaje automático, la optimización, la simulación y los modelos de dominio para desarrollar herramientas y tecnologías que proporcionen toda la información necesaria para ayudar a los directores de producción a diseñar líneas de fabricación, planificar la producción y mejorar la configuración del equipamiento para tomar decisiones efectivas y sostenibles que garanticen la calidad y seguridad del producto.
Objetivo
With a multidisciplinary consortium combining key skills in AI, manufacturing, edge computing and robotics, ASSISTANT aims to create intelligent digital twins through the joint use of machine learning (ML), optimization, simulation and domain models. The resulting tools permit to design and operate complex collaborative and reconfigurable production systems based on data collected from various sources such as IoT devices. ASSISTANT targets a significant increase in flexibility and reactivity, products/processes quality, and in robustness of manufacturing systems, by integrating human and machine intelligence in a sustainable learning relationship.
ASSISTANT provides decision makers with generative design based software for all manufacturing decisions. Rather than writing ad hoc code for each manufacturing sector, it provides a set of intelligent digital twins that self adapt to the manufacturing environment. It promote a methodology that enhances generative design with learning aspects of AI thanks to the data available in manufacturing. ASSISTANT aims to synthesize predictive/prescriptive models adjusted to the shop floor for each decision levels. Digital twins will be used as oracles by ML in order to converge towards models in phase with reality. This means that rather than writing specific code to cover a restricted set of goals/scenarios/hypotheses for a manufacturing system and a decision level, ASSISTANT will aim at learning models that can be used by standard optimization libraries. In this context, ML is used to predict parameter values, characterize parameters uncertainty, and acquire physical constraints. ASSISTANT will experiment this methodology on a significant panel of use cases selected for their relevance in the current context of the digital transformation of production in major manufacturing sectors undergoing rapid transformations like the energy, the industrial equipment, and automotive sectors which already make extensive use of digital twins.
Ámbito científico
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencessoftware
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-ICT-2020-1
Régimen de financiación
RIA - Research and Innovation actionCoordinador
91120 Palaiseau
Francia