Dynamic optimization of multipass milling operations via geometric programming

This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for multipass milling operations like plain milling and face milling. The developed strategy is based on the “maximum production rate” criterion and incorporates eight technological constraints. The optimum number of passes is determined via dynamic programming, and the optimal values of the cutting conditions are found based on the objective function developed for the typified criterion by using a non-linear programming technique called “geometric programming”. This paper also underlies the importance of using optimization strategies rather than handbook recommendations as well as pointing out the superiority of the multipass over the single-pass optimization approach.     

基于高质量和低振动的铣削参数优化方法研究

针对数控铣床在切削过程中产生的振动对工件表面质量的影响,提出以低振动和高表面质量为优化目标,对切削参数进行优化。以VDF-850A铣床为研究对象对45号钢进行铣削正交试验,通过建立振动采集系统,采集振动信号提取振动特征值并测量工件表面粗糙度值,应用最小二乘法拟合数据建立了振动和粗糙度数学模型。利用层次分析法确定两目标函数权重,使用平方和加权法对两目标函数加权拟合成综合目标评价函数,运用粒子群算法优化切削参数。试验结果表明:应用粒子群算法优化后的切削参数进行加工可有效的降低振动和提高表面质量。     

On cutting parameters selection for plunge milling of heat-resistant-super-alloys based on precise cutting geometry

In plunge milling operation the tool is fed in the direction of the spindle axis which has the highest structural rigidity, leading to the excess high cutting efficiency. Plunge milling operation is one of the most effective methods and widely used for mass material removal in rough/semi-rough process while machining high strength steel and heat-resistant-super-alloys. Cutting parameters selection plays great role in plunge milling process since the cutting force as well as the milling stability lobe is sensitive to the machining parameters. However, the intensive studies of this issue are insufficient by researchers and engineers. In this paper a new cutting model is developed to predict the plunge milling force based on the more precise plunge milling geometry. In this model, the step of cut as well as radial cutting width is taken into account for chip thickness calculation. Frequency domain method is employed to estimate the stability of the machining process. Based on the prediction of the cutting force and milling stability, we present a strategy to optimize the cutting parameters of plunge milling process. Cutting tests of heat-resistant-super-alloys with double inserts are conducted to validate the developed cutting force and cutting parameters optimization models.     

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

基于GA-BP的6061Al切削参数优化

针对6061Al铣削中表面粗糙度预测精度低、切削参数选择不合理的问题,提出一种基于遗传神经网络与遗传算法结合的优化模型,对6061Al切削参数进行优化。采用遗传神经网络(GA-BP)构建表面粗糙度预测模型;基于表面粗糙度预测,以材料去除率为目标函数构建切削参数优化模型;利用遗传算法进行优化求解,对6061Al切削参数进行优化。研究结果表明:所建预测模型表面粗糙度预测精度在97%以上;同时,优化模型能优化6061Al切削参数,达到较好的全局寻优效果,为铝合金工件铣削加工切削参数优化提供参考。     

Tool positioning error (TPE) characterisation in milling

Where the geometrical features so permit, the {workpiece–work-holding fixture} assembly is generally considered to be infinitely rigid. The {tool–tool-holder–spindle} assembly and the machine axes are then deformed under the action of the cutting forces. This deformation leads to a positioning error of the tool in relation to the theoretical position. With the aim of taking this positioning error into account, the inaccuracies obtained during end milling and side milling were experimentally modelled from the cutting conditions used for a given machine/mill/material triplet (TriM). Our “Virtual Worker” then used these models to predict machining errors according to the type of machining and to compensate for them.     

Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.     

基于DBSCAN算法的切削载荷状态智能识别算法

针对切削过程中的切削力状态识别以及切削参数的动态调整问题,提出了一种基于密度聚类算法的切削载荷智能识别算法;该方法通过对切削力载荷特性的识别、判断,使用密度聚类算法建立簇分类算法,以距离度量作为簇分类判据,以簇的核心点作为切削载荷学习对象,通过切削载荷理想值做出判断,实现对切削载荷的智能识别。论文应用该算法,结合Agent技术,对石材高速铣削条件下的切削力载荷状况进行了智能识别和可视化表述。研究表明,该算法能够实现对切削加工状态的智能识别与判断,并达到了对切削参数在线优化的目的,提高了加工效率与加工质量,为切削加工参数的智能优化奠定了理论基础。     

A new method of cutting force measurement based on command voltages of active electro-magnetic bearings

In this paper, a new indirect method of measuring dynamic cutting forces is proposed. Milling tests have been performed on a five-axis machine, Gambin 120CR, fitted out with an electro-spindle with magnetic bearings developed by the company S2M, and named SMB30. These bearings are not affected by friction and wear. An experimental approach has been developed to determine the cutting forces as a function of the measured command voltages of the milling spindle’s magnetic bearings. The spindle is treated as a “black box”, where the transfer functions linking the unknown cutting force with command voltages are established experimentally. The cutting forces calculated from the command voltages of magnetic bearings are in good agreement with the ones measured with a Kistler four-component dynamometer. This indirect method of cutting force determination provides a useful way to estimate tool wear and monitor product quality in high-speed milling on-line.     

Tool wear monitoring and breakage detection based on “intelligent filtering”

Several physical quantities can be used for indirect tool wear monitoring and breakage detection. Cutting forces are appropriate, since they determine the suitability of a tool for cutting. However, disturbances make the accurate and fast tool condition monitoring based upon force analysis difficult. To eliminate disturbances averaging or filtering within a predetermined bandwidth has often been applied. A new method of decomposing the force signal into components having close relationships with physical phenomena taking place during cutting is presented. The term “intelligent filtering” denotes decomposition based on on-line identification of model(s) of the cutting process. Application of “intelligent filtering” for the milling process with two cutting insert tools is discussed.