Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning

This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN?+?TiC?+?TiCN?+?TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature.     

A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations

This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness recognition (FN-M-ISRR) system, the goal of which is to predict surface roughness (Ra ) under multiple cutting conditions determined by tool material, workpiece material, tool size, etc. Surface roughness was measured indirectly by extrapolation from vibration signal and cutting condition data, which were collected in real-time by an accelerometer sensor. These data were analysed and a model was constructed using a neural fuzzy system. Experimental results showed that parameters of spindle speed, feedrate, depth of cut, and vibration variables could predict surface roughness (Ra) under eight different combinations of tool and workpiece characteristics. This neural fuzzy system is shown to predict surface roughness (Ra ) with 90% prediction accuracy during a milling operation.     

Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis

The vibrations on the cutting tool have a momentous influence for the surface quality of workpiece with respect to surface profile and roughness during the precision end-milling process. Singular spectrum analysis (SSA) is a new non-parametric technique of time series analysis and forecasting. The significant features of the cutting tool vibration signals from the sensors are extracted and transformed from the SSA-processed vibration signals. In the present study, SSA is applied to extract and transform the raw signals of the vibrations on the cutting tool for investigating the relationship between tool vibration and surface roughness in the precision end-milling process of hardened steel SCM440. In this experimental investigation, the spindle speed, feed rate, and cutting depth were chosen as the numerical factor; the cutting feed direction and holder type were regarded as the categorical factor. An experimental plan consisting of five-factor (three numerical plus two categorical) d-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test was conducted to visualize the effect of vibration of tooltip on the performance of surface roughness. With the experimental values up to 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the tool vibration and surface roughness. Results show that the effects of feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstable cutting process and exhibit the result of the worst machined surface. The amplitude of vibration signals along the cutting feed direction is generally larger than that along other direction. The spindle speed and tool holder type affect the stability of cutting tooltip during the cutting process.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

单点金刚石车削中刀具—工件相对振动的研究

在纳米级车削表面形成中刀具和工件间的相对振动为主要影响因素之一.现有相对振动的研究方法包括谱分析法、直接测量法和切削力分析法,均不能同时满足真实反映切削过程中的相对振动和很好地预测表面粗糙度的要求,致使纳米级表面粗糙度的预测精度较低.针对上述问题,通过分析和提取单点金刚石车削已加工表面轮廓数据、定义等效振幅的概念和计算...     

基于Elman网络的切削表面粗糙度预测方法

根据Elman神经网络模型能够逼近任意非线性函数的特点和具有反映系统动态特性的能力,讨论神经网络非线性、多因素预测原理及其拓扑结构的基础上,提出利用Elman神经网络建立切削表面粗糙度预测模型的方法;在Matlab及其神经网络工具箱的基础上,采用Elman神经网络对铝6061切削表面的粗糙度进行训练、预测、分析.结果表...     

基于表面粗糙度预测的数控车削加工物理仿真模型的研究

通过分析数控加工中表面粗糙度的产生原因,提出了表面粗糙度仿真系统模型的结构,建立了切削力、振动和表面粗糙度的数学模型,为通过物理仿真预测加工表面粗糙度提供了理论依据。     

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.     

Surface roughness model for turning

The effect of the cutting parameters on the surface roughness in a turning operation has been investigated using response surface methodology. The mathematical prediction models for surface roughness have been obtained for a common mild steel. The equation is used for determining the optimal cutting conditions for a required surface roughness.     

车削奥氏体不锈钢时冷却参数对刀具振动和表面粗糙度的影响

针对最小量润滑(MQL)技术在加工难切削材料时冷却能力不足的问题,分析了最小量冷却润滑(MQCL)条件下冷却参数对刀具振动和表面粗糙度的影响规律.设计了以田口法为基础的正交试验方案,并基于MQCL条件进行了相关切削试验.采用方差分析法、主效应图法、响应面法等方法并结合切削理论,分析了冷风温度、油液流量、风速、喷射面类型...     

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece

Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

Abstract

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

超声波振动精密切削GFRP的实验研究

为改善玻璃纤维增强塑料(Glass Fibre Reinforced Plastics)的切削加工性,提高加工精度和质量,采用超声波振动切削的方式对GFRP进行了精密切削加工.介绍了超声波振动切削的特性和GFRP的纤维束与切削速度方向的相位参数,相位参数沿圆周方向成周期性变化,变化周期为π.通过实验获得了不同切削条件下表面粗糙度的变化规律,粗糙度随相位角变化基本呈正弦规律,但在45°时粗糙度最大.振幅增大导致粗糙度明显下降.切削速度对粗糙度的变化曲线呈极值状态,在速度为100 m/min时粗糙度最小.进给量小于0.06 mm时,粗糙度呈下降趋势;大于0.06 mm时,粗糙度增加较快;而大于0.09 mm后粗糙度上升趋缓.切削深度对粗糙度的影响呈单调上升趋势.实验结果表明超声波振动切削可以使GFRP的加工表面粗糙度减少1倍,使加工质量得以提高.     

Optimisation of machining parameters for turning operations based on response surface methodology

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.     

Super-precision cylindrical machining

A study has been made of the theory and techniques of turning precise circular components. The possibility of such high accuracy cylindrical machining was analysed according to the principle of making a precise circle. In the study, a form of chucking-type preciison cylindrical machining was developed by combining an insensitive vibration cutting mechanism - using a main spindle system which features an air bearing - with superposition superfinishing. In the first process, the work is chucked on the main spindle and machined using a continuously and systematically pulsating cutting force. In the second process, the work is finished by a newly developed superposition superfinishing device which features equivalent grades of ultrasonic vibration stone. Key points of the techniques are a torsional vibration mode tool for producing accurate, high amplitude vibration of the cutting point, and a contrivance for making accurate movements of the superposition superfinishing device. Machined roundness of 0.1–0.2 μm and surface roughness of 0.03–0.09 μm R_max were obtained with plain carbon steels, stainless steels and hardened steels (H_RC 41, 53, 60). It is considered that turning to roundness ≈ 0, cylindricity ≈ 0 and surface roughness ≈ 0 can be realized by means of this new machining process and its lathe.     

In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals

In this work, an attempt has been made to use vibration signals for in-process prediction of surface roughness during turning of Ti–6Al–4V alloy. The investigation was carried out in two stages. In the first stage, only acceleration amplitude of tool vibrations in axial, radial and tangential directions were used to develop multiple regression models for prediction of surface roughness. The first and second order regression models thus developed were not found accurate enough (maximum percentage error close to 24%). In the second stage, initially a correlation analysis was performed to determine the degree of association of cutting speed, feed rate, and depth of cut and the acceleration amplitude of vibrations in axial, radial, and tangential directions with surface roughness. Subsequently, based on this analysis, feed rate and depth of cut were included as input parameters aside from the acceleration amplitude of vibrations in radial and tangential directions to develop a refined first order multiple regression model for surface roughness prediction. This model provided good prediction accuracy (maximum percentage error 7.45%) of surface roughness. Finally, an artificial neural network model was developed as it can be readily integrated into a computer integrated manufacturing environment.     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.