Thesis supervisor: Márton Takács
Location of studies (in Hungarian): BME Department of Manufacturing Science and Engineering Abbreviation of location of studies: GTT
Description of the research topic:
a.) Preliminaries:
Nowadays 3D printing procedures are gaining even more importance, but conventional chip removal operations will surely remain essential methods of modern manufacturing technology. The reason for that is the capability of high performance material removal in the case of wide range of different materials, high precision and excellent surface quality, and the opportunity of cost optimized machining. The demand for producing parts of devices made of special and hard materials is continuously increasing. Machining of materials with high hardness (>45 HRC) poses a challange to the chip removal process.
Framework of Industry 4.0 is introduced by many manufacturing companies. It means that huge number of production data have to be collected, analyzed, and used in order to develop decision supporting and predictive models. This models can give good prediction about the characteristics of the machined parts, and remaining useful life of cutting tool at the moment.
Integration of advanced machining processes of hard materials into the framwork of Industry 4.0 is of major importance, because it can realize a cost effective and well controlled manufacturing process.
b.) Aim of research:
Main goal of this research work is integration of machining processes of hard materials into the Framework 4.0, and efficiency improvement by means of tools of information technology.
c.) Tasks, main items, necessary time:
Literature survey regarding hard machining and hard cutting (1st semester)
Summary of the state-of-the-art knowledge related to tool condition monitoring, digital twin, and machine learning. (2nd semester).
Specification and set-up of measurement system for recording machining data. Specification of workpiece materials and cutting parameters. Design of experiments (3rd semester).
Carrying out hard cutting experiments. Collection of force and vibration data. Investigation of tool degradation (4th semester).
Signal analysis. Preparation of model development. (5th semester)
Prescriptive, descriptive and predictive data analytics. Development of predictive models for surface roughness and tool wear prediction by using machine learning techniques. (5th, 6th and 7th semester)
Implementation, validation and demonstration of developed model for machining of hard alloys. Verifying the effectiveness. (7th semester)
Publication of the results (between 3rd and 8th semester)
Preparing of scientific thesis (7th and 8th semester)
d.) Required equipment:
5-axis micro milling centre, 4-axis turning center, ultra precision tuning late, force and vibration sensors, data acquisition system, digital and confocal microscopes, surface roughness measuring units, evaluation software, FEM software are required, which are available at the department, or at partner departments.
e.) Expected scientific results:
Deep understanding of characteristics of hard machining processes related to improvement of quality and efficiency. Predictive models for tool life at hard turning. Predictive model for surface roughness at hard machining processes. Decision supporting system for optimal time of tool changing.
f.) References:
J C, Jáuregui ; J R, Reséndiz ; S, Thenozhi ; T, Szalay ; Á, Jacsó ; M, Takács: Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring, IEEE ACCESS 6 pp. 6400-6410., 11 p. (2018)
Balázs, B Z ; Takács, M, Finite element modelling of thin chip removal process, IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING 426 Paper: 012002 (2018)
M, Takács ; B Z, Balázs ; J C, Jáuregui: Dynamical Aspects of Micro Milling Process, In: Tomaz, Pepelnja; Zlatan, Car; Jan, Kudlacek (szerk.) International Conference on Innovative Technologies : IN-TECH 2017, Faculty of Engineering University of Rijeka, (2017) pp. 181-184., 4 p.
Takács M, Verő B: Material Structural Aspects of Micro-Scale Chip Removal, MATERIALS SCIENCE FORUM 414-415, 2003, pp. 337-342.
M Takács, B Verő, I Mészáros: Micromilling of Metallic Materials, JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 138, 2003, pp. 152-155.
Required language skills: English Number of students who can be accepted: 1
Deadline for application: 2022-10-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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