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Enhancing Production Processes through AI and Machine Learning: A Stellantis Case Study in Industry 4.0
keywords DATA ANALYSIS, INDUSTRY 4.0, MACHINE LEARNING, AI, MACHINE LEARNING
Reference persons DANILO GIORDANO, MARCO MELLIA
Research Groups DAUIN - GR-04 - DATABASE AND DATA MINING GROUP - DBDM, SmartData@PoliTO
Thesis type APPLIED RESEARCH, INDUSTRY
Description The digitization of the industry has ushered in a new industrial era known as Industry 4.0. In this context, machines have gained intelligence and the ability to collect vast amounts of data. This data is crucial for conducting data-driven analyses, leveraging Artificial Intelligence and Machine Learning methodologies to understand and optimize production lines.
Stellantis, a global leader in vehicle manufacturing, has implemented machinery capable of monitoring the operation of their machines and the processes used in the fabrication of automotive body shells.
The aim of this thesis is to analyze this data in order to identify operational patterns to cluster together similar machine behaviours and detect any anomalies in their performance. The primary objective is to develop methodologies based on Artificial Intelligence and Machine Learning that expand our understanding of production processes.
The thesis is part of a partnership with Stellantis S.p.A., a car manufacturer leader and will see the collaboration of the interdepartmental SmartData@PoliTO centre.
Required skills Python and data science libraries
Deadline 30/08/2024
PROPONI LA TUA CANDIDATURA