Thesis supervisor: István Németh
Location of studies (in Hungarian): BME Gyártástudomány és -technológia Tanszék (Department of Manufacturing Science and Engineering) Abbreviation of location of studies: GTT
Description of the research topic:
a.) Antecedents:
The analysis of the degradation processes of machine tool components has been dealt with in the framework of previous international research projects at the Department of Manufacturing Science and Engineering. Methodology and software tools have been developed to determine the optimal maintenance strategies and optimal preventive maintenance cycles as well as the expected remaining useful life. The basic objective of the research is to extend this methodology with new measuring and analysing methods of the load-dependent degradation processes and with the prediction of these degradation processes.
b.) Aim of research:
The aim is to build a bearing test pad with which similar load cases of the bearings of milling spindles can be produced, as well as the loads and the condition of the bearings can be measured and recorded continuously. Then reliability, maintenance and degradation parameters can be calculated and predicted on the basis of the resulting time series.
c.) Tasks, main items, necessary time:
• Literature research: loading and testing methods and equipment of rolling bearings; methods of data collection and analysis; degradation models considering historical load levels and involving artificial intelligence methods; accelerated lifetime tests. (0.5 years)
• Mechanical and mechatronic design and construction of the test pad (1.5 years).
˗ Design of the shaft, bearing and drive of the spindle.
˗ Design of the loading mechanism: it should be possible to apply both radial and axial loads in an adjustable way, both in static and dynamic (chatter-like) manner.
˗ Selection and design of the mounting of sensors (e.g., force, speed, acceleration, temperature, acoustic emission, etc.).
˗ Selection of data collection and analysis tools.
˗ Installation and commissioning of the test pad.
• Definition of methods for determining key performance indicators related to degradation, historical load levels, reliability and maintainability (0.5 years).
• Developing artificial intelligence methods to predict the degradation processes and the reaming useful life. (0.5 years)
• Operating the test pad, collection and processing of data, application of artificial intelligence methods to make predictions. (2.5 years).
• Validation of the results of the prediction methods (0.5 years).
• Writing the dissertation (0.5 years).
d.) Required equipment:
The necessary computing and diagnostic tools are available at the Department of Manufacturing Science and Engineering.
e.) Expected scientific results:
Methodology for measuring, analysing and predicting the degradation processes of the spindle bearings of machine tools.
f.) References:
1. István Németh, Ádám Kocsis, Donát Takács, Basheer W. Shaheen, Márton Takács, Angelo Merlo, Amit Eytan, Luisa Bidoggia, and Paolo Olocco: Maintenance schedule optimisation for manufacturing systems, Proceedings of the 4th IFAC Workshop on Advanced Maintenance Engineering, Service and Technology, AMEST 2020, September 10-11, 2020. Cambridge, UK, pp. 319-324
2. M. Surico, R. Ricatto, A. Merlo, I. Németh, A. Sardelis, M. Villoslada, E. Montejo, N. Frenkel, P. Aivaliotis, I. M. de la Pera Celada, J. Sidiropoulos, A. Eytan, A. Papavasileiou, F. Aggogeri: PROGRAMS project approach to maintenance management, Proceedings of the 4th IFAC Workshop on Advanced Maintenance Engineering, Service and Technology, AMEST 2020, September 10-11, 2020. Cambridge, UK, pp. 313-318
3. Alice Reina, Ádám Kocsis, Angelo Merlo, István Németh, and Francesco Aggogeri: Maintenance decision support for manufacturing systems based on the minimization of the life cycle cost, Procedia CIRP, Volume 57, 2016, pp. 674-679
4. EASE-R3: Integrated framework for a cost-effective and ease of repair, renovation and re-use of machine tools within modern factory, European research project, EU FP7 NMP, NMP2-LA-2013-608771, Duration: 1 July 2013 – 30 June 2016
5. PROGRAMS: Prognostics based Reliability Analysis for Maintenance Scheduling, European research project, EU H2020-FOF, Contract number: 767287, Duration: 1 October 2017 – 31 March 2021
6. SMART-GRIP: Integrated software suite for component matchmaking and maintenance planning for robotic grippers, Subproject of the umbrella project Market 4.0 (EU H2020-NMBP-PLUG-2018-IA7), Duration: 1 April 2021 – 31 March 2022
Required language skills: English Number of students who can be accepted: 1
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