%0 Conference Proceedings
%T Smart Factory Competitiveness Based on Real Time Monitoring and Quality Predictive Model Applied to Multi-stages Production Lines
%+ EnginSoft
%+ Università degli Studi di Padova = University of Padua (Unipd)
%A Gramegna, Nicola
%A Greggio, Fabrizio
%A Bonollo, Franco
%Z Part 4: Data-Driven Applications in Smart Manufacturing and Logistics Systems
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Novi Sad, Serbia
%Y Bojan Lalic
%Y Vidosav Majstorovic
%Y Ugljesa Marjanovic
%Y Gregor von Cieminski
%Y David Romero
%I Springer International Publishing
%3 Advances in Production Management Systems. Towards Smart and Digital Manufacturing
%V AICT-592
%N Part II
%P 185-196
%8 2020-08-30
%D 2020
%R 10.1007/978-3-030-57997-5_22
%K Cost model
%K Industry 4.0
%K Digital twin
%K Overall efficiency
%K Zero defect manufacturing
%K Data mining
%K Predictive modeling
%Z Computer Science [cs]Conference papers
%X Smart Factories are complex manufacturing ecosystems where the converging of ICT and operational technologies and competences drive the digital transformation. Smart manufacturing operations planning and control program, as defined by NIST, implement advances in measurement science that enable performance, quality, interoperability, wireless and cybersecurity standards for real-time prognostics and health monitoring, control, and optimization of smart manufacturing systems.The traditional production processes and plants are evolving following this digitalization combining the long experience and the AI-driven methods to improve the production efficiency, to accelerate the fine-tuning and real-time adjustment of the process parameters oriented to the zero defect quality. The digitalization of multi-stages production processes (e.g. foundry) plays a key role in competitiveness introducing new integrated platform to monitor the process through an intelligent sensors network and predict quality and cost of castings in real-time.The application presented in this paper is the main outcome of EU FP7-MUSIC project giving a new age to the traditional multi-stages production. The actual regional project PreMANI (POR FESR 2014–2020) is a new extended application of AI-driven digital twin in manufacturing process and quality control. This paper demonstrates the applicability of data-driven digital twins to small and medium-sized enterprises (SME) and to complex manufacturing sectors integrating the process monitoring with advance data mining and cognitive approach to predict the quality, the efficiency vs cost and react in real-time with the support of decision support system.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-03635668/document
%2 https://inria.hal.science/hal-03635668/file/504014_1_En_22_Chapter.pdf
%L hal-03635668
%U https://inria.hal.science/hal-03635668
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-592