Abstract

Ball-end milling is widely used in industrial applications, such as mold manufacturing and aerospace industries. One of many difficulties in milling research is the milling force, which is often crucial to the component’s processing efficiency and quality. In this paper, a method for predicting ball-end cutter milling force and its probabilistic characteristics is presented. First, a model of milling force prediction is established by integrating the differential milling forces. Then, a method of identifying milling force coefficients is suggested based on the proposed model, which considered both the mean value and the amplitude of the milling force. Based on the proposed milling force prediction model, milling force probabilistic analysis while considering randomness of cutting parameters is performed. To improve computational efficiency, the function relationship between cutting parameters and the milling force is reconstructed by adaptive Kriging model. Furthermore, trained Kriging model is employed to analyze the probabilistic characteristics of the milling force. Both experimental and computational results indicate that the method proposed in this paper has high accuracy and efficiency.

摘要

球头铣削广泛用于工业应用，例如模具制造和航空航天工业。铣削研究的众多难点之一是铣削力，它往往对零件的加工效率和质量至关重要。本文提出了一种预测球头铣刀铣削力及其概率特性的方法。首先，通过整合微分铣削力建立铣削力预测模型。然后，基于所提出的模型提出了一种识别铣削力系数的方法，该方法同时考虑了铣削力的平均值和幅度。基于所提出的铣削力预测模型，进行了考虑切削参数随机性的铣削力概率分析。为了提高计算效率，采用自适应Kriging模型重构切削参数与铣削力的函数关系。此外，训练的克里金模型被用来分析铣削力的概率特性。实验和计算结果均表明本文提出的方法具有较高的精度和效率。