Simeone, Alessandro (2013) Multi-Sensor Process Monitoring in Turning of Inconel 718. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Multi-Sensor Process Monitoring in Turning of Inconel 718
Creators:
CreatorsEmail
Simeone, Alessandroalessandro.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:
nomeemail
Carrino, Luigiluigi.carrino@unina.it
Tutor:
nomeemail
Teti, Robertoroberto.teti@unina.it
Date: 31 March 2013
Number of Pages: 207
Uncontrolled 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

Abstract

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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