Simeone, Alessandro (2013) Multi-Sensor Process Monitoring in Turning of Inconel 718. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Multi-Sensor Process Monitoring in Turning of Inconel 718 |
| Creators: | Creators Email Simeone, Alessandro alessandro.simeone@unina.it |
| Date: | 31 March 2013 |
| Number of Pages: | 207 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Chimica, dei Materiali e della Produzione Industriale |
| Scuola di dottorato: | Ingegneria industriale |
| Dottorato: | Tecnologie e sistemi di produzione |
| Ciclo di dottorato: | 25 |
| Coordinatore del Corso di dottorato: | nome email Carrino, Luigi luigi.carrino@unina.it |
| Tutor: | nome email Teti, Roberto roberto.teti@unina.it |
| Date: | 31 March 2013 |
| Number of Pages: | 207 |
| Keywords: | Sensor Monitoring, Inconel 718, Turning, Neural Networks, Surface Integrity, Sensor Fusion, Multiple Sensor Monitoring, Tool State Identification |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/16 - Tecnologie e sistemi di lavorazione |
| Date Deposited: | 03 Apr 2013 15:36 |
| Last Modified: | 24 Jul 2014 07:06 |
| URI: | http://www.fedoa.unina.it/id/eprint/9288 |
| DOI: | 10.6092/UNINA/FEDOA/9288 |
Collection description
This thesis work was developed in conjunction with the activities of the EC FP7 Adaptive Control of Manufacturing Processes for a New Generation of Jet Engine Components (ACCENT) Project (See section 1.4). Most of the experimental activities were carried out at AVIO SpA facilities, Pomigliano d’Arco, Naples; Avio SpA is an industrial partner in the EC FP7 ACCENT Project. The goals of this thesis work are explained below. First of all, the design and realization of an experimental campaign of turning tests on a nickel base alloy of aeronautical interest (Inconel 718) was carried out in an industrial environment. A multi sensor monitoring system, endowed with diverse sensing units was designed, assembled, calibrated and employed during machining tests in order to acquire different sensor signals on an online basis. Raw signals acquired were subjected to conventional and advanced signal analysis methods in order to extract significant features useful for decision making on process conditions. This thesis work includes material characterization tests carried out to investigate the surface integrity of the workpiece as well as the state of the tool wear with the scope of correlating these conditions to sensor signals features. By the implementation of a decision making support system, sensor signal features extracted by signal processing techniques were utilized for the identification of defects in the workpiece due to the machining process, as revealed by the material characterization tests. Decision making was carried out by diverse Neural Network pattern recognition paradigms, designed and implemented for the purpose.
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