SS304车削时刀片界面温度的量纲分析及人工神经网络仿真

IF 0.4 Q4 METALLURGY & METALLURGICAL ENGINEERING
A. Kulkarni, S. Chinchanikar, V. Sargade
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引用次数: 0

摘要

介绍。在加工过程中,产生的温度对加工性能有更广泛和更关键的影响。在加工过程中,功率消耗主要转化为刀具切削刃附近的热量。在塑性变形过程中所做的几乎所有功都转化为热。由于切削温度对刀具寿命和整体加工性能的影响很大,研究人员对切削温度的测量进行了大量的研究。工作目的:研究在SS304车削过程中,考虑切削参数和刀具涂层类型的影响,切屑-刀具界面温度。在一定的切削深度下,通过改变切削速度和进给量来测量未涂层和PVD单层TiAlN和多层TiN/TiAlN涂层硬质合金刀具的切削界面温度。此外,本文还尝试利用量纲分析和人工神经网络模拟建立了一个预测刀具-切屑界面温度的模型,以更好地理解这一过程。调查方法。实验在切削速度(140 ~ 260 m/min)、进给量(0.08 ~ 0.2 mm/rev)和切削深度为1mm不变的情况下进行。采用刀具-工件热电偶原理测量刀具-刀具界面温度。校准装置旨在建立加工过程中产生的电动势(EMF)与切削温度之间的关系。利用统计量纲分析和人工神经网络模型对刀具-切屑界面温度进行了预测。根据切削条件测量切向切削力和切屑宽度、厚度等切屑属性,这是进行量纲分析仿真的前提条件。结果和讨论。与TiN/TiAlN涂层相比,采用PVD涂层的TiAlN碳化物制成的刀具在切削-刀具界面处的温度更低。随着切削速度的增加,切屑-刀具界面温度显著升高,切屑截面积和切削比压力随之增大。然而,当使用带有多层TiN/TiAlN涂层的硬质合金刀具时,可以观察到较低的切削力,这可以归因于该刀具的前表面产生的较低的摩擦系数,用于流动切屑。另一方面,未涂层硬质合金刀具的切削力最大。结果表明,所建立的模型能够以5%的绝对误差预测刀具与切屑界面的温度。然而,人工神经网络模型的平均绝对误差最低,为0.78%,因此可以可靠地用于预测SS304车削过程中的切屑-刀具界面温度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dimensional analysis and ANN simulation of chip-tool interface temperature during turning SS304
Introduction. During machining, the resulting temperature has a wider and more critical impact on machining performance. During machining, the power consumption is mainly converted into heat near the cutting edge of the tool. Almost all the work performed during plastic deformation turns into heat. Researchers have put a lot of effort into measuring the cutting temperature during machining, as it significantly affects tool life and overall machining performance. The purpose of the work: to investigate the temperature of the chip-tool interface, taking into account the influence of cutting parameters and the type of tool coating during SS304 turning. The chip-tool interface temperature is measured by changing the cutting speed and feed with a constant cutting depth for uncoated and PVD single-layer TiAlN and multi-layer TiN/TiAlN coated carbide tools. In addition, an attempt is made to develop a model for predicting the temperature of the chip-tool interface using dimensional analysis and ANN simulating to better understand the process. The methods of investigation. Experiments are carried out with varying the cutting speed (140-260 m/min), feed (0.08-0.2 mm/rev) and a constant cutting depth of 1 mm. The chip-tool interface temperature is measured using the tool-work thermocouple principle. The Calibration Setup is designed to establish the relationship between the produced electromotive force (EMF) and the cutting temperature during machining. Statistical dimensional analysis and artificial neural network models have been developed to predict the temperature of the chip-tool interface. Tangential cutting force and chip attributes such as chip width and thickness are also measured depending on the cutting conditions, which is a prerequisite for dimensional analysis simulation. Results and Discussion. A tool made of TiAlN carbide with PVD coating had a lower temperature at the chip-tool interface than a tool with TiN/TiAlN coating. It has been observed that the chip-tool interface temperature increases prominently with the cutting speed, followed by the chip cross-sectional area and the specific cutting pressure. However, a lower cutting force was observed when using a carbide tool with a multi-layer TiN/TiAlN coating, which can be attributed to a lower coefficient of friction created by the front surface of this tool for flowing chips. On the other hand, the greatest cutting force was observed in uncoated carbide tools. It was noticed that the developed models allow predicting the temperature of the chip-tool interface with an absolute error of 5%. However, the lowest average absolute error of 0.78% was observed with the ANN model and, therefore, can be reliably used to predict the chip-tool interface temperature during SS304 turning.
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Obrabotka Metallov-Metal Working and Material Science
Obrabotka Metallov-Metal Working and Material Science METALLURGY & METALLURGICAL ENGINEERING-
CiteScore
1.10
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50.00%
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26
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