Assessment of cutting force coefficient identification methods and force models for variable pitch and helix bull-nose tools

IF 4.6 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Joshua Priest , Sabino Ayvar-Soberanis , Javier Dominguez-Caballero , Peace Onawumi , Zekai Murat Kilic , David Curtis
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引用次数: 0

Abstract

The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average Fx, Fy and Fz cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.
评估可变螺距和螺旋牛鼻子刀具的切削力系数识别方法和力模型
机械方法通常用于预测和优化铣削过程中的切削力,以防止刀具破损、减少刀具磨损、降低形状误差并提高表面质量。要实施这种方法,必须计算切削力系数(CFCs),这是加工过程的力学特征。本研究使用基于快速测试(RT)优化的机械 CFC 识别方法,对可变螺距和螺旋牛鼻铣刀的切削力预测精度进行了比较,该方法只需要一次角度切削,并增加径向啮合,而传统的机械方法则需要多次直切削。同时还开发了一种混合技术,将进给量和径向啮合的变化结合起来。传统的径向、切向和轴向(RTA)力模型也与摩擦力和法向斜面(UV)力模型进行了比较,后者与牛鼻子刀具所需的局部刀具斜面和倾斜角无关。RT 和开发的混合 CFC 识别方法与 UV 力模型预测的平均 Fx、Fy 和 Fz 切削力误差分别在 7.1%、4.3% 和 3.8% 以内。这些方法的精确度略低于传统方法,但它们具有显著的工业效益,因为它们可分别用于识别单次切削或任何刀具路径的切屑载荷变化的氯氟化碳。RTA 力模型对平均切削力的预测与 UV 力模型相似,但在实验设计空间的极端角落,使用快速 RT 测试方法,UV 力模型的误差更小。
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来源期刊
CIRP Journal of Manufacturing Science and Technology
CIRP Journal of Manufacturing Science and Technology Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
6.20%
发文量
166
审稿时长
63 days
期刊介绍: The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.
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