| KEYWORD |
Waste and losses reduction by pervasive materials and operation tracking
keywords INDUSTRY 4.0, IOT, M2M COMMUNICATIONS, REAL TIME MONITORING, TRACKING
Reference persons ANDREA ACQUAVIVA, ENRICO MACII
Research Groups ELECTRONIC DESIGN AUTOMATION - EDA
Thesis type EXPEIRMENTAL, IN COMPANY
Description 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.
Required skills programming, machine learning (basics)
Notes Thesis in collaboration with FCA, including a period in company at the production plants
Deadline 31/12/2017
PROPONI LA TUA CANDIDATURA