Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

铣削加工粗糙度的智能预测方法

提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法.首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律.利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值.实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个～3个数量级,预测精度高10倍左右.     

Model-based identification of tool runout in end milling and estimation of surface roughness from measured cutting forces

This paper presents a model-based approach for the identification of tool runout and the estimation of surface roughness from measured cutting forces. In the first part of the paper, the effect of tool runout on variations in the cutting forces and the effect on surface roughness generation are studied. Thereby, several influencing parameters are identified and examined systematically. Based on theoretical considerations, systematic relationships between tool runout, resultant process force variations, and surface roughness characteristics are deduced. The sensitivity of process force variation is investigated for varying runout parameters by experimental tests. In the next part, the model-based runout identification method is developed, which identifies runout parameters accurately from the measured process forces. The approach has been tested extensively and was verified by measured runout parameters and the correlation of surface roughness characteristics of the machined workpiece. The performance of the developed approach is demonstrated in the final part by comparing the result of the model-based surface reconstruction with the measured surface topography.     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353

Journal of Mechanical Science and Technology - In the present work, an attempt has been made to use Box-Cox transformation with response surface methodology to develop improve surface roughness...     

Cutting force model considering tool edge geometry for micro end milling process

The analysis of the cutting force in micro end milling plays an important role in characterizing the cutting process, as the tool wear and surface texture depend on the cutting forces. Because the depth of cut is larger than the tool edge radius in conventional cutting, the effect of the tool edge radius can be ignored. However, in micro cutting, this radius has an influence on the cutting mechanism. In this study, an analytical cutting force model for micro end milling is proposed for predicting the cutting forces. The cutting force model, which considers the edge radius of the micro end mill, is simulated. The validity is investigated through the newly developed tool dynamometer for the micro end milling process. The predicted cutting forces were consistent with the experimental results.     

PCBN刀具铣削灰铸铁HT200时合理几何参数的选择

本文采用单因素实验法,研究PCBN刀具铣削灰铸铁HT200的几何参数对刀具耐用度的影响规 律,根据最大刀具耐用原则给出了PCBN刀具铣削HT200的合理几何参数范围.     

Optimization of cutting conditions for minimum residual stress,cutting force and surface roughness in end milling of S50C medium carbon steel

Residual stresses are usually imposed on a machined component due to thermal and mechanical loading. Tensile residual stresses are detrimental as it could shorten the fatigue life of the component; meanwhile, compressive residual stresses are beneficial as it could prolong the fatigue life. Thermal and mechanical loading significantly affect the behavior of residual stress. Therefore, this research focused on the effects of lubricant and milling mode during end milling of S50C medium carbon steel. Numerical factors, namely, spindle speed, feed rate and depth of cut and categorical factors, namely, lubrication and milling mode is optimized using D-optimal experimentation. Mathematical model is developed for the prediction of residual stress, cutting force and surface roughness based on response surface methodology (RSM). Results show that minimum residual stress and cutting force can be achieved during up milling, by adopting the MQL-SiO₂ nanolubrication system. Meanwhile, during down milling minimum residual stress and cutting force can be achieved with flood cutting. Moreover, minimum surface roughness can be attained during flood cutting in both up and down milling. The response surface plots indicate that the effect of spindle speed and feed rate is less significant at low depth of cut but this effect significantly increases the residual stress, cutting force and surface roughness as the depth of cut increases.     

Selection of optimal cutting conditions for pocket milling using genetic algorithm

Pocket milling is the most known machining operation in the domains of aerospace, die, and mold manufacturing. In the present work, GA-OptMill, a genetic algorithm (GA)-based optimization system for the minimization of pocket milling time, is developed. A wide range of cutting conditions, spindle speed, feed rate, and axial and radial depth of cut, are processed and optimized while respecting the important constraints during high-speed milling. Operational constraints of the machine tool system, such as spindle speed and feed limits, available spindle power and torque, acceptable limits of bending stress and deflection of the cutting tool, and clamping load limits of the workpiece system, are respected. Chatter vibration limits due to the dynamic interaction between cutting tool and workpiece are also embedded in the developed GA-OptMill system. Enhanced capabilities of the system in terms of encoded GA design variables and operators, targeted cutting conditions, and constraints are demonstrated for different pocket sizes. The automatically identified optimal cutting conditions are also verified experimentally. The developed optimization system is very appealing for industrial implementation to automate the selection of optimal cutting conditions to achieve high productivity.     

Study on the prediction model of surface roughness for side milling operations

The paper presents a feasibility study on prediction of surface roughness in side milling operations using the different polynomial networks. A series of experiments using S45C steel plates is conducted to study the effects of the various cutting parameters on surface roughness. The different polynomial networks for predicting surface roughness are developed using the abductive modeling technique and based on the F-ratio to select their input variables. The results show that the developed models achieve high predicting capability on surface roughness, especially for the case of smaller flank wear of peripheral cutting edge. Hence, it can be concluded that the developed polynomial-network models posses promising potential in the application of predicting surface roughness in side milling operations.     

An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation

The aim of this study is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of AISI 1040 steel material with TiAlN solid carbide tools under wet condition. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness. Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model exploiting a full factorial design of experiments. Genetic algorithm (GA) supported with the tested ANN is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. GA improves the surface roughness value from 0.67 to 0.59 μm with approximately 12% gain. Then, machining time has also decreased from 1.282 to 1.0316 min by about 20% reduction based on the cutting parameters before and after optimization process using the analytical formulas. The final measurement experiment has been performed to verify surface roughness value resulted from GA with that of the material surface by 3.278% error. From these results, it can be easily realized that the developed study is reliable and suitable for solving the other problems encountered in metal cutting operations as the same as surface roughness.     

Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

The role of stepover ratio in prediction of surface roughness in flat end milling

Surface roughness prediction studies in end milling operations are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The stepover ratio is usually neglected without investigating it. The aim of this study is to discover the role of the stepover ratio in surface roughness prediction studies in flat end milling operations. In realising this, machining experiments are performed under various cutting conditions by using sample specimens. The surface roughnesses of these specimens are measured. Two ANN structures were constructed. First of them was arranged with considering, and the second without considering the stepover ratio. ANN structures were trained and tested by using the measured data for predicting the surface roughness. Average RMS error of the ANN model considering stepover ratio is 0.04 and without considering stepover ratio is 0.26. The first model proved capable of prediction of average surface roughness (Ra) with a good accuracy and the second model revealed remarkable deviations from the experimental values.     

In-process surface roughness prediction system using cutting vibrations in turning

Machining is a complex process in which many variables can affect the desired results. Among them, surface roughness is a widely used index of a machined product quality and, in most cases, is a technical requirement for mechanical products since, together with dimensional precision, it affects the functional behavior of the parts during their useful life, especially when they have to be in contact with other materials. In-process surface roughness prediction is, thus, extremely important. In this work, an in-process surface roughness estimation procedure, based on least-squares support vector machines, is proposed for turning processes. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constitute the input information to the system. Experimental results show that the proposed system can predict surface roughness with high accuracy in a fast and reliable way.     

Correlation between different cutting conditions,surface roughness and dimensional accuracy when ball end micro milling material measures with freeform surfaces

Abstract

Material measures are used to calibrate topography measuring instruments. Micro-milling is a suitable process for the manufacturing of areal material measures in particular of material measures featuring freeform surfaces. To improve their surface quality and to minimize their deviations from the target geometry, the cutting parameters feed per tooth respectively feed rate and spindle speed are examined in this study. Dependent on the varied parameters the deviation of the manufactured topography from its target geometry, the deviation to the nominal surface texture parameters and the short-wavelength roughness parameters are evaluated. Two different ball end micro-mills and two different target geometries are chosen to investigate whether the observed dependence on the varied parameters are valid independent from the tool and the target geometry. It is illustrated that the feed rate has a large influence on the dimensional accuracy: the dynamic properties of the axes are identified as reason for the decreasing amplitude with increasing feed rate. The spindle speed only influences the short wavelength roughness and has a minor influence on the surface quality.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

A dynamic surface roughness model for face milling

This paper presents a newly developed mathematical model for surface roughness prediction in a face-milling operation. The model considers the static and the dynamic components of the cutting process. The former includes of cutting conditions as well as the edge profile and the amount of runout of each insert set into a cutter body. The latter introduces the dynamic characteristics of the milling process. It is verified that such a model predicts the maximum or the arithmetic mean surface roughness value through the cutting experiments. The model can evaluate the surface texture of the precision parts machined with face milling.