Riduzione delle perdite e degli sprechi nei processi industriali tramite tracking di materiali e procedure
Gruppi di ricerca ELECTRONIC DESIGN AUTOMATION - EDA
Tipo tesi SPERIMENTALE, IN AZIENDA
Descrizione Plants of the future are expected to be able to reduce (ideally, eliminate) waste and losses possible leading to poor quality of products and services, injuries, faults. In order to detect such wastes, performing an extensive tracking of materials or components used in the production line (where possible) can be a promising approach. The correlation with other data from environmental sensors and machine logs can be used to detect potential sources of wastes and losses for a given process control context.
The main objective of this thesis is to exploit knowledge of industrial processes, sensing and tracking IoT technologies to perform deep learning and data mining to characterize operations and materials in the production line to detect wastes and inefficiencies.
Conoscenze richieste programmazione, machine learning (basics)
Note Tesi in collaborazione con FCA, prevede un periodo in azienda presso gli stabilimenti di produzione
Scadenza validita proposta 31/12/2017 PROPONI LA TUA CANDIDATURA