Abd Aziz, Syazxerlin (2018) Prediction Of Tool Wear Characteristics In Turning On Inconel 718 : Experimentation And Simulation. Masters thesis, Universiti Teknikal Malaysia Melaka.
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Prediction Of Tool Wear Characteristics In Turning On Inconel 718 - Experimentation And Simulation.pdf - Submitted Version Download (1MB) |
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Abstract
Inconel 718 is a superalloy that has very strong mechanical properties due to an excellent yield strength at elevated temperature, capable to withstand thermal shock and corrosion resistance. In this study, the turning process is chosen as material removal operation. A bar stock of Inconel 718 and uncoated tungsten carbide were used as workpiece and cutting tool respectively. The turning operation evaluation was conducted in two different techniques which are experimental machining and simulation modelling. The input parameters selected in this study were spindle speed, depth of cut and feet rate. The chosen spindle speed parameters are 717 rpm, 876 rpm, 1035 rpm and 1194 rpm. The other two parameters which are depth of cut and feet rate parameters remain fixed as 1.0 mm and 0.1 mm/rev. Each set of cutting parameter underwent four repetitions of machining operation in order to access variability in this study. The aim of study is to establish and evaluate correlation between spindle speed and tool wear characteristics for both experimental machining and simulation modelling techniques in turning of Inconel 718 operation. The equipment used in this study was CNC lathe machine for experimental machining, thermal imager for capturing thermogram image of temperature distribution during turning process for maximum values. Besides, tool maker microscope and optical microscope were used for analysing and observing physical and structural changes of tool wear characteristics on the cutting tool edge. For the simulation modelling technique, DEFORM 3D software used in order to predict the tool wear characteristics that occurred after simulation process is completely done. The output response obtained from DEFORM 3D software is in terms of graphical image, graph chart and numerical values. The pre processor, simulator and post processor were generated based on the actual experimental machining characterizations which were mechanical properties, geometry dimensions and cutting conditions. The data analysis method for this study were regression and correlation analysis by using Minitab software. The hypothesis of this study is stated that the tool wear characteristics increase as the spindle speed increased. The tool wear characteristics generation are influenced by spindle speed as heat generated between contacted area of cutting tool and workpiece. The positive results obtained from the experimental machining and simulation modelling which indicated that rising in spindle speed tends to increase tool wear characteristics. In the experimental machining, result shows that flank wear length, notch wear length, crater wear length, chip formation and maximum temperature increased due to rising in spindle speed. Besides, the simulation modelling also determines that maximum temperature, total velocity, tool wear-interface temperature, tool wear-interface pressure, tool wear-sliding velocity, effective strain rate, effective strain, nodal heat, total force, folding angle, effective stress, tool wear-wear rate, tool wear-total wear depth, minimum distance, surface area, surface expansion ratio, damage and maximum shear stress increased due to rising in spindle speed.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Machining, Metal-cutting, Machine-tools, Surface roughness, Tool Wear, Turning On Inconel 718, Experimentation And Simulation |
| Subjects: | T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Library > Tesis > FKP |
| Depositing User: | Mohd Hannif Jamaludin |
| Date Deposited: | 18 Dec 2019 15:54 |
| Last Modified: | 15 Mar 2022 11:07 |
| URI: | http://eprints.utem.edu.my/id/eprint/23887 |
| Statistic Details: | View Download Statistic |
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