Modelling and simulation of whirling process based on equivalent cutting volume

The thread whirling is an efficient and precise machining process for manufacturing of screws. The shaping motion of whirling is complex and difficult to model. In this paper, a novel model basing on equivalent cutting volume is proposed. The cutting force and the chip morphology are investigated to validate the model. The simulation of cutting force is in good agreement with the experimental results with error less than 16.5%. A chip with saw-toothed edges is obtained from simulation and for experimental verification. A case study on the effect of the tool edge geometry on cutting forces is also presented. The simulation results show that the tool edge geometry greatly influences the cutting forces. The tool with round edge is a good choice for reducing the cutting forces. The ratio of a_c/R_e holds the balance in selecting the parameter of cutting conditions. The model is applicable for the simulation of whirling process and can be used for parameter optimisation of the cutting tool edge.     

Improved prediction of stability lobes in milling process using time series analysis

In this paper, a new method is presented for prediction of cutting forces, surface texture and stability lobes in end milling operation based on time series analysis. In the approach, an equivalent damping ratio is defined for the cutting zone while the damping ratio of non-cutting zone is determined by experimental modal analysis. Using correlation dimension criterion, the simulation and experimental force signals are compared to anticipate the value of process damping by assessing the variation of correlation dimension for both signals. The effect of cutter deflections and run out are taken into account. Moreover, the stability lobes are predicted by considering the variation of process damping with cutting conditions. The feasibility of the proposed algorithm is verified experimentally for machining of Aluminum 7075-T6. Comparison of experiment results against simulation results indicates that the improved model can accurately predict cutting forces, surface texture and stability lobes for low radial immersion.     

Estimation of cutting forces and surface roughness for hard turning using neural networks

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.     

Position-oriented process monitoring in milling of thin-walled parts

Improving machining performance of thin-walled parts is of great significance in aviation industry, since most aviation parts are characterized by large size, complex shape, and thin-walled structure. Machining process monitoring is the essential premise to improve the machining performance. In order to improve the machining quality and efficiency, this paper presents a position-oriented process monitoring model based on multiple data during milling process, and corresponding solution is provided. Through obtaining the internal data set of the numerical control (NC) system during machining, it is possible to correlate the cutting position with monitoring signals including cutting force, acceleration, and spindle power. Then, process optimization is realized to improve the machining quality and efficiency based on the monitoring results. Machining tests are conducted on aircraft structural part as well as blade part, and the experimental results show this method provides a significant insight into the machining process of thin-walled part and contributes to the process optimization. By using feedrate optimization, time consumption for the rough milling process of one titanium alloy part reduced from 19.1 h to 14.4 h and the number of cutter consumption dropped from 5 to 3. And according to the result of position-oriented process monitoring, the machining strategies were optimized to reduce vibration and avoid chatter, thereby improving the machining quality.     

Prediction of cutting forces in 3-axes milling of sculptured surfaces directly from CAM tool path

In the present work a new approach for the modelling of milling is described. The cutting forces are calculated for milling operations directly from the tool path provided by a Computer Assisted Manufacturing program. The main idea consists in using tool position points coming from CAM data in order to calculate the local inclination angle of the generated surface and then the tool engagement in the machined material. A good approximation for global and local cutting forces can be obtained when an analytical model able to predict the cutting forces for 3-axes milling is used. Two approaches are proposed to calculate the local cutting forces to show the versatility of the method. The first method uses a thermomechanical approach using a Johnson & Cook constitutive law while the second is based on classical cutting coefficients. Some results are presented for wavelike form and free form machining tests and are compared with experimental data obtained in roughing and finishing of 42CrMo4 steel. Results are satisfactory and the capability of the method to predict the resultant surface roughness is shown.     

A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming

Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.     

Mechanistic modelling of the milling process using an adaptive depth buffer

A mechanistic model of the milling process based on an adaptive and local depth buffer is presented. This mechanistic model is needed for speedy computations of the cutting forces when machining surfaces on multi-axis milling machines. By adaptively orienting the depth buffer to match the current tool axis, the need for an extended Z-buffer is eliminated. This allows the mechanistic model to be implemented using standard graphics libraries, and gains the substantial benefit of hardware acceleration. Secondly, this method allows the depth buffer to be sized to the tool as opposed to the workpiece, and thus improves the depth buffer size to accuracy ratio drastically. The method calculates tangential and radial milling forces dependent on the in-process volume of material removed as determined by the rendering engine depth buffer. The method incorporates the effects of both cutting and edge forces and accounts for cutter runout. The simulated forces were verified with experimental data and found to agree closely. The error bounds of this process are also determined.     

面向直线电机驱动数控铣削加工智能控制系统

以双直线电机驱动进给机构为被控对象,以配套的SIMODRIVE611D为控制系统,建立了伺服系统的数学模型,给出了直线电机电流与切削力的关系,确定了以直线进给轴电机驱动电流为反馈对象、以进给速度为控制输出量的控制原理。围绕进给轴恒电流加工控制目标,选择了模糊控制作为直线驱动进给加工过程的控制方法。结果表明模糊控制有更快的响应时间和更好的抗干扰性,并可有效地保护刀具和提高20%的铣削加工效率。     

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al₂O₃ + TiN and Al₂O₃) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R², RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.     

Off-line feed rate scheduling using virtual CNC based on an evaluation of cutting performance

This paper presents an advanced technology to automatically determine optimum feed rates for 2 axis CNC machining, without requiring the expertise of a machinist or the information contained in a machining data handbook. Present CAM technology does not consider important physical properties such as cutting forces and machined surface errors. However, the virtual machining system developed in this study can simulate real machining for a given set of NC codes. An analytical model for off-line feed rate scheduling is formulated to improve productivity and machining accuracy. Using this model, it is possible to regulate the cutting force, which drastically improves the overall form accuracy of the machined surface.     

球头铣刀柔性铣削力建模与仿真

本文建立了球头铣刀柔性铣削力模型,研究了任意进给方向的球头铣刀铣削力模型,在模型建立过程中考虑了刀具偏心、刀具变形和刀具振动物理影响因素,推导出了综合物理因素影响下的瞬时切屑厚度表达式.在此基础上对球头铣刀瞬时铣削力进行仿真对比分析,验证了建立模型的正确性.     

Dynamic NC simulation of milling operations

To increase productivity in manufacturing, accurate cutting-simulation systems have increasingly been used to study the performance of machining processes. A new dynamic cutting-simulation system that can simulate the dynamic behaviour of the milling cutting force along the programmed NC toolpath is presented in the paper. The radial and axial depths of cut in the cutting process are extracted from a geometric cutting-simulation system on a workstation. Then, the radial and axial depths of cut and the other given cutting parameters are transmitted to a mechanistic model of the milling process from which the dynamic cutting force is obtained. There is good agreement between the simulated and measured cutting forces.     

A semi-analytical approach to un-deformed chip boundary theory and cutting force prediction in face-hobbing of bevel gears

Rule-of-thumb based design for cutting tools and machining settings in face-hobbing of bevel gears result in cutting tool failures and quality issues. Lack of a virtual machining environment, to efficiently obtain the instantaneous un-deformed chip geometry and predict cutting forces in face-hobbing, causes undesirable production costs in industries. In the present paper, semi-analytical representation of the projection of the un-deformed chip on the rake face of the cutting blades is presented. The proposed approach is drastically fast and more accurate in comparison with numerical methods and can be implemented in a virtual gear machining environment. The cutting system intricate geometry, multi-axis machine tool kinematic chains and the variant cutting velocity along the cutting edge are taken into consideration to obtain the chip geometry efficiently. Then, cutting forces are predicted during face-hobbing by implementing oblique cutting theory using the derived chip geometry and converting face-hobbing into oblique cutting. The proposed methods are applied on two case studies of face-hobbing of bevel gears, and the chip geometry is derived and the cutting forces are predicted.     

Prediction of workpiece elastic deflections under cutting forces in turning

One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel^© workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.     

立铣加工过程中的微元铣削力建模分析

针对单纯通过高速切削技术制造某些大型飞机零件过程中,存在难以控制的加工变形和较强表面残余拉应力分布等突出问题,以直齿和螺旋齿立铣加工过程为研究对象,基于微元切削机理,通过对刀齿铣削过程的分析,建立动态铣削加工仿真模型,导出铣削面积、铣削力、主轴扭矩、铣削功率与切削用量的关系.数值模拟结果与试验值较吻合,表明该模型可以实现动态铣削力预测,优化切削用量.     

Assessment of micro turning machine stiffness response and material characteristics by fuzzy rule based pattern matching of cutting force plots

In micro-nano systems technology (MNST), application of mechanical based machining operations such as micro turning, micro milling, micro EDM have shown promising trends to produce micro parts in batch scale. In order to ensure reproducibility better understanding on micro cutting process dynamics and sensitivity of machine stiffness and material characteristics becomes critical. In this paper, a methodology has been developed to assess machine stiffness and material dependent characteristics and demonstrated for micro turning operations conducted on DT-110 micro machining center. In this method, authors incorporate pattern matching algorithm to compare run data image of cutting force plots with that of reference plot. The reference plots of cutting forces v/s time were drawn from simulation run data computed from the micro turning process models. The run data plots of cutting force v/s time were drawn from the processed signal data obtained from the dynamometer during machining operation. The plots were fragmented into patterns and Euclidean distance computed between pair patterns of reference and measured cutting forces v/s time plot image represents the changes happened in machining conditions. This has been used to perform backward calculation to assess the machine stiffness response and material characteristic constants variations over machining time. In order to perform these comparative pattern error adjustments between reference and measured cutting force plots a fuzzy rule based algorithm has been developed.     

Three dimensional modeling and finite element simulation of a generic end mill

The geometry of cutting flutes and the surfaces of end mills is one of the crucial parameters affecting the quality of the machining in the case of end milling. These are usually represented by two-dimensional models. This paper describes in detail the methodology to model the geometry of a flat end mill in terms of three-dimensional parameters. The geometric definition of the end mill is developed in terms of surface patches; flutes as helicoidal surfaces, the shank as a surface of revolution and the blending surfaces as bicubic Bezier and biparametric sweep surfaces. The proposed model defines the end mill in terms of three-dimensional rotational angles rather than the conventional two dimensional angles. To validate the methodology, the flat end milling cutter is directly rendered in OpenGL environment in terms of three-dimensional parameters. Further, an interface is developed that directly pulls the proposed three-dimensional model defined with the help of parametric equations into a commercial CAD modeling environment. This facilitates a wide range of downstream technological applications. The modeled tool is used for finite element simulations to study the cutting flutes under static and transient dynamic load conditions. The results of stress distribution (von mises stress), translational displacement and deformation are presented for static and transient dynamic analysis for the end mill cutter flute and its body. The method described in this paper offers a simple and intuitive way of generating high-quality end mill models for use in machining process simulations. It can be easily extended to generate other tools without relying on analytical or numerical formulations.     

Optimal control for chatter mitigation in milling—Part 1: Modeling and control design

Active structural methods constitute a promising way to mitigate chatter vibrations in milling. This paper presents an active system integrated into a spindle unit. Two different optimal control strategies are investigated. The first one only considers the dynamics of the machine structure in the controller design and minimizes the influence of cutting forces on tool tip deviations. The second one takes explicitly the process interaction into account and attempts to guarantee the stability of the overall closed-loop system for specific machining conditions. The modeling and formulation used for both strategies are presented in this first part. A simulation allows the comparison of their respective working principle. The validation of the proposed concept in experimental conditions is described in the second part.     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Global stability predictions for flexible workpiece milling using time domain simulation

This paper describes the use of peak-to-peak (PTP) force diagrams for machining stability prediction and validates its suitability for milling processes where the workpiece is considerably more flexible than the machine-tool system. These diagrams result from numerous executions of a time domain simulation which includes both the tool and workpiece dynamics and a mechanistic force model. The applicability of the PTP force diagram is validated experimentally through peripheral milling tests of thin-walled structures. Measured and simulated cutting forces are compared. It is shown that the PTP diagrams offer the global stability information which is provided by the traditional lobe diagram, while preserving the detailed, local information provided by time domain simulation.