Toward a zero defects manufacturing process with Artificial Intelligence applications
External reference persons Giorgio Pasquettaz
Thesis type DATA ANALYSIS, EXPERIMENTAL
Description In the automotive plants the manufacturing processes are complex systems which are subject to different conditions influencing the quality of the final components. Nowadays, these processes are monitored to extract data usable to perform data-driven studies able to discover insights about the manufacturing line.
Among the other goals, data can be used:
- to describe the manufacturing process;
- to identify anomalies in the process conditions causing component defects;
- to analyse the root cause of an anomaly;
- to understand how to react bringing the process back to a normal working condition.
The thesis is part of a collaboration with FCA, so the candidate will have the opportunity to work together with experts in the automotive field. It aims at studying the laser welding process, in which robots weld together small components. For this study, the candidate will use Artificial Intelligence (AI), and Machine Learning (ML) methodologies to analyze data related this process. The first goal is to understand what is the normal laster welding behaviour and what is the laser welding behaviour when a defect in the component occurs. The second one is to identify the reason why the system drifts from the normal behaviour causing the defect.
For more information contact firstname.lastname@example.org.
Required skills - Data mining (strong)
- Programming (high level programming languages Python or similar)
Notes The thesis is part of a collaboration with FCA, so the candidate may spend part of the thesis at the CRF (Centro Ricerche Fiat).
Deadline 20/02/2021 PROPONI LA TUA CANDIDATURA