Tool wear modelling using micro tool diameter reduction for micro-end-milling of tool steel H13
Tool wear modelling using micro tool diameter reduction for micro-end-milling of tool steel H13
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DOI：
10.1007/s00170-019-04575-4
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
C. S. Manso;S. Thom;E. Uhlmann;C. L. F. de Assis;E. D. Del Conte
中科院分区：
文献类型：
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作者：
C. S. Manso;S. Thom;E. Uhlmann;C. L. F. de Assis;E. D. Del Conte
随着全球产品和设备小型化的趋势，对微型元器件的需求日益增加。微铣削是最有前途的微尺度生产工艺之一，与常规铣削不同，由于尺寸效应引入了最小切屑厚度等现象，使得微铣削过程的预测变得困难。在微铣削的挑战中，刀具寿命和刀具磨损是突出的。了解微铣削中的刀具磨损和建模对于保持工件的质量和几何公差具有挑战性和重要性。研究了H13工具钢微铣削过程中刀具磨损引起的刀具直径减小量的建模方法。为了获得考虑直径减小量时影响刀具磨损的切削参数，进行了切削试验。采用直径d = 400 μm的TiAlN（氮化钛铝）涂层微刀具进行干式全槽铣削。在切削长度为1182 mm的情况下，采用了3种水平的每齿进给量（fz= 2 μm、4 μm和5 μm）和2种主轴转速水平（n= 30,000 rpm和46,000 rpm），并进行了评价。结果表明，较低水平的每齿进给量和主轴转速导致较高的刀具磨损，总直径减少超过22%。通过方差分析确定了影响刀具磨损的切削参数的大小，模型验证符合统计要求，决定系数R2 = 83.5%，表明该方法在微铣削中使用直径减小建模来预测刀具磨损的可行性。
Micro components have been demanded increasingly due to the global trend of miniaturization of products and devices. Micro milling is one of the most promising processes for micro-scale production and differs from conventional milling due to the size effect introducing phenomena like the minimum chip thickness, making the prediction of micro milling process hard. Among challenges in micro milling, tool life and tool wear can be highlighted. Understanding tool wear and modelling in micro milling is challenging and essential to maintaining the quality and geometric tolerances of workpieces. This work investigates how to model the diameter reduction of a tool caused by tool wear for micro milling of H13 tool steel. Machining experiments were carried out in order to obtain cutting parameters affecting tool wear by considering the diameter reduction. Dry full slot milling with TiAlN (titanium aluminium nitride)-coated micro tools of diameterd= 400 μm was performed. Three levels of feed per tooth (fz= 2 μm, 4 μm and 5 μm) and two spindle speed levels (n= 30,000 rpm and 46,000 rpm) were used and evaluated over a cutting length oflc= 1182 mm. The results show that lower levels of feed per tooth and spindle speed lead to higher tool wear with a total diameter reduction over 22%. The magnitude of the cutting parameters affecting tool wear was determined by ANOVA (analysis of variance), and the model validation meets the statistical requirements with a coefficient of determinationR2= 83.5% showing the feasibility of the approach to predict tool wear using diameter reduction modelling in micro milling.