Descrizione del progetto
Una soluzione rivoluzionaria per ottimizzare i sistemi di produzione
Per mantenere la produzione competitiva, i manager devono progettare e gestire sistemi di produzione collaborativi e riconfigurabili complessi che utilizzino al massimo le nuove tecnologie. Tuttavia, tutto questo richiede ancora molta ricerca per liberare il pieno potenziale degli strumenti digitali per la produzione. Il progetto ASSISTANT, finanziato dall’UE, mira a sviluppare soluzioni rivoluzionarie per l’industria manifatturiera utilizzando l’intelligenza artificiale per ottimizzare i sistemi di produzione. Una delle chiavi di volta di ASSISTANT è la creazione di gemelli digitali intelligenti. Combinando apprendimento automatico, ottimizzazione, simulazione e modelli di dominio, ASSISTANT sviluppa strumenti e soluzioni che forniscono tutte le informazioni necessarie ad aiutare i manager di produzione a progettare linee di fabbricazione, programmare la produzione e migliorare le impostazioni delle macchine per decisioni efficaci e sostenibili che garantiscano la qualità e la sicurezza dei prodotti.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
91120 Palaiseau
Francia