Takács Márton
Efficiency improvment of micro chip removal processes in the frame of Industry 4.0


Intézmény: Budapesti Műszaki és Gazdaságtudományi Egyetem
gépészeti tudományok
Pattantyús-Ábrahám Géza Gépészeti Tudományok Doktori Iskola

témavezető: Takács Márton
helyszín (magyar oldal): Gyártástudomány és -technológia Tanszék
helyszín rövidítés: GTT

A kutatási téma leírása:

1. Introduction
Modern manufacturing technology requires a continuous development to ensure economic and sustainable machining of parts of devices. Demands for the high precision and ultra-precision machining providing high geometrical accuracy and excellent surface quality are growing. There is an increasing request for part miniaturization, too. Wide spread application of NC and CNC machines enables collecting information and data about the ongoing machining process by on-line measurements. This expands the experimental base of the technology planning, and decreases its costs, therefore the task of experimental research can be moved from the laboratories to the production plants. Data analysis and creating predictive models for tool degradation and properties of machined quality are essential requirements for implementation of micro chip removal properties into the new revolution of production, which is called as Industry 4.0. Digital twin can support optimizing the cutting process.

2. Aim of the research
Main goal of this research work is improving efficiency of thin micro machining process carried out in a cyber-physical production system by application of methods of information technology. Deep understanding of specialties of micro chip removal processes (such as micro milling and hard turning) considering size effect is aimed. Efficiency improvement can be realized by ensuring optimal time of tool changing, and application of optimal cutting parameters in order to achieve the desired quality. Huge amount of reliable data of different operations is required, and then prescriptive, descriptive and predictive data analytics will be carried out. Predictive models of tool wear and surface roughness will be developed by using artificial intelligence techniques. Digital twin of tooling system will be introduced. Models are planned to be implemented, and validated in the case of machining of advanced materials.

3. Tasks, time necessary
Literature survey regarding micro machining and hard cutting (1st semester)
Summary of the state-of-the-art knowledge related to tool condition monitoring, digital twin, and artificial intelligence. (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 thin chip removal experiments. Collection of force and vibration data. Investigation of tool degradation (4th semester).
Signal analysis. Preparation of digital twin models of tool systems. (5th and 6th semester)
Prescriptive, descriptive and predictive data analytics. Development of predictive models for surface roughness and tool wear prediction by using artificial intelligence 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)

4. Necessary 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 roughnes measuring units, evalutaion software, FEM software are required, which are available at the department, or at partner depertmants.
5. Expected scientific results
Deep understanding of characteristics of micro machining processes related to improvmenet of quality and efficiency. Predictive models for tool life of micro milling and hard turning. Predictive model for surface roughness at micro chip removal processes. Basic concept of decision supporting system for optimal time of tool changing.

6. Literature
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.

ajánlott nyelvtudás (magyar oldal): english
felvehető hallgatók száma: 1

Jelentkezési határidő: 2021-03-23

2020. X. 20.
ODT online ülés
Az ODT következő, online ülésére 2020. november 6-án 10.00 órakor kerül sor.

Minden jog fenntartva © 2007, Országos Doktori Tanács - a doktori adatbázis nyilvántartási száma az adatvédelmi biztosnál: 02003/0001. Program verzió: 2.2358 ( 2017. X. 31. )