| International Conference and Exhibition in Advanced Materials and Microscopy 2017 | |
| Comparative Investigation on Tool Wear during End Milling of AISI H13 Steel with Different Tool Path Strategies | |
| 材料科学;物理学 | |
| T Adesta, Erry Yulian^1 ; Riza, Muhammad^2 ; Avicena, A.^1 | |
| Department of Manufacturing and Materials Engineering, International Islamic University Malaysia, Malaysia^1 | |
| Jurusan Teknik Mesin, Universitas Bandar, Lampung, Indonesia^2 | |
| 关键词: Central composite designs; Coated carbide insert; Machining parameters; Optimization of cutting conditions; Planning and control; Prediction model; Response surface method; Vertical machining; | |
| Others : https://iopscience.iop.org/article/10.1088/1757-899X/343/1/012020/pdf DOI : 10.1088/1757-899X/343/1/012020 |
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| 学科分类:材料科学(综合) | |
| 来源: IOP | |
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【 摘 要 】
Tool wear prediction plays a significant role in machining industry for proper planning and control machining parameters and optimization of cutting conditions. This paper aims to investigate the effect of tool path strategies that are contour-in and zigzag tool path strategies applied on tool wear during pocket milling process. The experiments were carried out on CNC vertical machining centre by involving PVD coated carbide inserts. Cutting speed, feed rate and depth of cut were set to vary. In an experiment with three factors at three levels, Response Surface Method (RSM) design of experiment with a standard called Central Composite Design (CCD) was employed. Results obtained indicate that tool wear increases significantly at higher range of feed per tooth compared to cutting speed and depth of cut. This result of this experimental work is then proven statistically by developing empirical model. The prediction model for the response variable of tool wear for contour-in strategy developed in this research shows a good agreement with experimental work.
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| Comparative Investigation on Tool Wear during End Milling of AISI H13 Steel with Different Tool Path Strategies | 336KB |
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