Condition-based Maintenance For The Optimization Of Smart Manufacturing Processes Using Infrared Thermography

Résumé: In The framework of European Union initiative, ERASMUS + program, Capacity Building in the field of Higher Education project entitled "Algerian National Laboratory for Maintenance Education” ANLMED, project no. 586035-EPP-1-2017-1-DZ EPPKA2-CBHE-JP (https://anlmed.com/) was funded and implemented starting March 2018. One of the objectives of this project is related to the mobilities of students from four Algerian universities, including the Université M’Hamed Bougara de Boumerdes (UMBB) to European universities. During the mobility in “Dunarea de Jos” University of Galati (UDJG) was elaborated and completed the Master thesis entitled “Conditional Based-Maintenance for the Optimization of Intelligent Manufacturing Processes by Infrared Thermography” (Maintenance conditionnelle pour l'optimisation des procédés de fabrication intelligents par thermographie infrarouge). In the thesis are elaborated and developed, in the theoretical part, the basics of maintenance, in general, and the in particular the fundamental concepts Conditional Based-Maintenance (CBM). Later on, the concept related to condition monitoring (CM) techniques, in different industrial applications are presented, such as: oil analysis, infrared thermography (IRT), vibration analysis, ultrasonic analysis, acoustic analysis and electrical analysis. An entire chapter is dedicated to infrared thermography. This choice is appointed by the potential and efficiency of this diagnostic method, that allows to detect temperature variation, revealing the appearance of major defects that may affect the operational safety of the production equipment. The experimental part was conducted in the Mechatronics Laboratory of "Dunarea de Jos" University of Galati - Faculty of Engineering, Department of Manufacturing Engineering and included the conditional monitoring of smart manufacturing process, in this case of Additive Manufacturing (AM) process using Fusion Deposit Modelling (FDM) technology, performed using two 3D printers: Creality CR-10S Pro and Ultimaker 2+. The main purpose of the research was to analyse and test three different methods of conditional-based maintenance using three types of conditional monitoring, namely: vibration (by vibrometers), sound (by sound level meters) and temperature (by thermal camera and infrared thermography). Moreover, a probabilistic model using Bayesian Networks was designed, in order to develop a condition-based maintenance model for the AM process. This technique and approach can represent a successful integration of a large number of data monitoring sets and complex modelling and analysis capabilities are achieved that can lead in the end at an optimisation of the AM process. In conclusion, the thesis addresses complex fundamental and experimental researches with wide application in Industrial Engineering field, and specifically in Condition-Based Maintenance

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