| KEYWORD |
Management of the manufacturing know-how through the integration between the Manufacturing Execution System and the PLM system.
Thesis in external company
keywords INDUSTRY 4.0, MACHINE LEARNING, AI, PLM
Reference persons GIULIA BRUNO, PAOLO CHIABERT, FRANCO LOMBARDI
Research Groups Gestione della conoscenza nello sviluppo prodotto/processo
Thesis type SYSTEM ANALYSIS AND SIMULATION
Description Product Lifecycle Management (PLM) is the organizational process that enables the managing of products along their entire lifecycle: from the hull to the hell. The main aim of PLM is to provide a backbone that supports people, data, and procedures involved in product development, manufacturing, and aftermarket services so that costs and quality are under control. Nowadays, special attention is dedicated to OKPs (One -of a- Kind Products) that still exhibit high development costs in spite of new technological processes (rapid prototyping, additive manufacturing, etc.). The improvement of product quality and process reliability asks for maximizing the cooperation among the involved actors.
Within this framework, the development of manufacturing work cycles and tools, including the adaptation of product features and functional requirements is a fundamental step in the product development phase.
The purpose of the thesis is focused on this step, where manufacturing companies re-use their existing know-how to make the OKP quality and cost compliant with the facilities available in the plant (in-door), or from suppliers (tiers).
In particular, the thesis will address a case-study in the field of car body prototypes, with the aim to develop an intelligent system, based on the integration between PLM and MES platform, able to:
- capture the manufacturing experiences by classifying and storing relative data and models,
- make such information available and exploitable in the proper product development phases,
- schedule internal and external manufacturing work cycles and activities in agreement with the expected due dates.
Welcome background: Production planning and control; Process simulation.
Deadline 29/09/2021
PROPONI LA TUA CANDIDATURA