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.     

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.     

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...     

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.     

The development of an in-process surface roughness adaptive control system in end milling operations

An in-process surface roughness adaptive control (ISRAC) system in end milling operations was researched and developed. A multiple regression algorithm was employed to establish two subsystems: the in-process surface roughness evaluation (ISRE) subsystem and the in-process adaptive parameter control (IAPC) subsystem. These systems included not only machine cutting parameters such as feed rate, spindle speed, and depth of cut, but also cutting force signals detected by a dynamometer sensor. The multiple-regression-based ISRE subsystem predicted surface roughness during the finish cutting process with an accuracy of 91.5%. The integration of the two subsystems led to the ISRAC system. The testing resulted in a 100% success rate for adaptive control, proving that this proposed system could be implemented to adaptively control surface roughness during milling operations. This research suggests that multiple linear regression used in this study was straightforward and effective for in-process adaptive control.     

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.     

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.     

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.     

Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6

Surface roughness has an important role in the performance of finished components. End ball milling is used for achieving high surface quality, especially in complex geometries. Depending on the cutting conditions selected for ball end milling, different milling strategies can be applied. The produced surface quality is greatly affected from the selected milling strategy. The present paper examines the influence of the milling strategy selection on the surface roughness of an Al7075-T6 alloy. A number of cutting parameters are tested (axial and radial depth of cut, feed rate, inclination angles φ and ω) in order to perform 96 experiments and their results are processed using regression analysis and analysis of variance. All possible milling strategies are considered (vertical, push, pull, oblique, oblique push and oblique pull) and for each one of them, a mathematical model of the surface roughness is established, considering both the down and up milling. All models are statistically validated and experimentally verified, and can be used within the limits of the investigating cutting conditions. The polynomials produced are of the third order and the statistically most significant parameters are presented.     

高速铣削过程中表面粗糙度变化规律的试验研究

在高速铣削试验的基础上 ,研究分析切削速度与进给量对加工表面粗糙度的影响。试验数据表明 ,切削速度的提高有利于改善加工表面粗糙度 ,当切削速度超过某一范围后 ,随切削速度的进一步提高 ,加工表面粗糙度的降低并不明显 ,有时还会使表面粗糙度增加。根据试验结果 ,对具体工件材料与刀具材料匹配选择合理的切削速度与进给量范围 ,可以获得最小加工表面粗糙度值     

Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and analytical definition of material removal rate are employed in the construction of optimization problem. The optimization problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm. Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for a specific cutting condition in end milling Inconel 718.     

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.     

Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm

This paper presents a new approach to determine the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling artificial neural network (ANN) and harmony search algorithm (HS). In this regard, advantages of statistical experimental design technique, experimental measurements, analysis of variance, artificial neural network and harmony search algorithm were exploited in an integrated manner. To this end, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward neural network exploiting experimental data. The optimization problem was solved by harmony search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. The obtained results show that the harmony search algorithm coupled with feed forward neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.     

A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert

The prime factor for selecting equipment is its performance capability and reliability without compromising on quality. Materials for aerospace application such as aluminum and its alloys have limited applications because of their complications in machining, effectively and economically. There is no further development in raising the effectiveness above the optimal level in cutting tool materials. The surface roughness influences the determination of the quality of the product. The present study focuses on finding optimal end milling process parameters by considering multiple performance characteristics using grey fuzzy approach. In this work, Aluminum Alloy 6082T6 (AA6082T6) is used as workpiece material which was end milled using Aluminum Chromo Nitride (AP3) coated milling insert. Three process performance parameters namely Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR) were optimized. The grey output is fuzzified into five membership functions and also with twenty-seven rules. Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) shows that the maximum contribution in the depth cut is (31.785%) followed by feed (28.212%). Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been developed with the help of the same input values compared to the performance of the fuzzy logic model. With the help of detailed analysis, it has been found that the fuzzy logic based model gives more reasonable results when compared to ANFIS model.     

基于声发射信号的铣削加工表面粗糙度预测技术研究

使用声发射技术对铣削过程进行监测,通过对声发射信号进行频域分析,比较不同频段的能量比来在线预测加工后的表面粗糙度.     

高速铣削过程中表面粗糙度变化规律的试验研究

在高速铣削试验的基础上,研究切削速度与进给量对加工表面粗糙度的影响。     

数控铣削中曲面加工的粗糙度预测

针对计算机辅助制造中数控铣床上常用的 3种刀具路径的包络线进行了分析 ,给出了不同路径下包络线残留高度的计算公式 ,并对影响粗糙度的因素进行了讨论 ,该公式可用于表面粗糙度的预测。提出了另一种自适应刀具路径的规划方法 ,该方法可使加工表面得到基本一致的表面粗糙度