Selection of optimum cutting condition of cobalt-based superalloy with GONNS

Machining of new superalloys is challenging. Automated software environments for determining the optimal cutting conditions after reviewing a set of experimental results are very beneficial to obtain the desired surface quality and to use the machine tools effectively. The genetically optimized neural network system (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data with minimal operator involvement. Genetic algorithm (GA) obtains the optimal operational condition by using the neural networks. A feed-forward backpropagation-type neural network was trained to represent the relationship between surface roughness, cutting force, and machining parameters of face-milling operation. Training data were collected at the symmetric and asymmetric milling operations by using different cutting speeds (V _c), feed rates (f), and depth of cuts (a _p) without using coolant. The surface roughness (Ra_asymt, Ra_symt) and cutting force (Fx_asymt, Fy_asymt, Fz_asymt, Fx_symt, Fy_symt, Fz_symt) were measured for each cutting condition. The surface roughness estimation accuracy of the neural network was better for the asymmetric milling operation with 0.4% and 5% for training and testing data, respectively. For the symmetric milling operations, slightly higher estimation errors were observed around 0.5% and 7% for the training and testing. One parameter was optimized by using the GONNS while all the other parameters, including the cutting forces and the surface roughness, were kept in the desired range.     

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.     

SURFACE ROUGHNESS PREDICTION MODEL FOR ULTRAPRECISION TURNING ALUMINIUM ALLOY WITH A SINGLE CRYSTAL DIAMOND TOOL

A surface roughness model utilizing regression analysis method is developed for predicting roughness of ultra-precision machined surface with a single crystal diamond tool. The effects of the main variables, such as cutting speed, feed, and depth of cut on surface roughness are also analyzed in diamond turning aluminum alloy. In order to predict and control the surface roughness before ultraprecision machining, constrained variable metric method is used to select the optimum cutting conditions during process planning. A lot of experimental results show that the model can predict the surface roughness effectively under a certain cutting conditions .     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R _a value has been obtained as 1.1999???m and the corresponding predicted surface roughness value is 1.1859???m, which implies greater accuracy.     

TC17钛合金低温铣削表面粗糙度预测

进行了TC17钛合金低温铣削试验,研究了不同切削条件下的已加工表面粗糙度.采用回归分析方法建立了表面粗糙度经验模型,研究了射流温度、每齿进给量、铣削速度和径向切削深度对表面粗糙度的影响规律.基于BP神经网络建立了表面粗糙度预测模型,并与经验模型进行了对比分析.研究结果表明,基于经验模型表面粗糙度值与参数间存在强相关性(...     

PCD刀具车削超硬铝合金的切削性能研究

以Al7075-T6为加工对象,通过车削试验对PCD刀具车削超硬铝合金的三向动态切削力和表面粗糙度展开研究,建立基于BP神经网络的切削力和表面粗糙度预测模型。结果表明:随着切削用量三要素的变化,切削力变化显著;对于表面粗糙度而言,背吃刀量、进给量和切削速度之间无交互作用;基于L-M优化算法的BP神经网络对样本的拟合度高,且对切削力和表面粗糙度的预测精度高。     

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.     

Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm

Nowadays, manufacturers rely on trustworthy methods to predict the optimal cutting conditions which result in the best surface roughness with respect to the fact that some constraining functions should not exceed their critical values because of current restrictions considering competition found among them in delivering economical and high-quality products to the stringent customers in the shortest time. The present research deals with a modified optimization algorithm of harmony search (MHS) coupled with modified harmony search-based neural networks (MHSNN) to predict the cutting condition in longitudinal turning of X20Cr13 leading to optimum surface roughness. To this end, several experiments were carried out on X20Cr13 stainless steel to attain the required data for training of MHSNN. Feed-forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and the MHS algorithm was used to find the constrained optimum of surface roughness. Furthermore, simple HS algorithm was used to solve the same optimization problem to illustrate the capabilities of the MHS algorithm. The obtained results demonstrate that the MHS algorithm is more effective and authoritative in approaching the global solution compared with the HS algorithm.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.     

Machined surface quality prediction models based on moving least squares and moving least absolute deviations methods

Surface roughness is often taken as an indicator of the quality of machined work pieces. Achieving the desired surface quality is of great importance for the product function. The paper analyzes the influence of the cutting depth, feed rate, and number of revolutions on surface roughness. The obtained results of the experimental research conducted on the work piece “diving manifold,” were used to determine the coefficients by different numerical methods of the same prediction model. The results of the surface roughness provided by the prediction functions generated in this work were compared with the results of the surface roughness obtained by using neural networks. The assessment of the surface roughness provided by models and neural networks can facilitate the work of less experienced technologists and thus shorten the time of production technology preparation. The obtained results show that the total mean square deviation in models obtained by the application of the moving linear least squares and the moving linear least absolute deviations methods is nevertheless considerably higher than by the application of the neural network method.     

Optimization of turning process using artificial intelligence technology

In the current work, some experiments were performed based on a design of experiment (DOE) technique called full factorial design. The experimental results are discussed in statistical analysis, and the system was modeled using the artificial neural network (ANN) and subsequently optimized by a genetic algorithm (GA). The statistical analysis shows that the main effects and some 2-interaction effects affect the surface roughness and flank wear. The results show that the feed rate, nose radius, and approach angle have a significant effect on the flank wear and the surface roughness, but the cutting velocity has a significant effect on the flank wear alone. The optimum values of cutting parameters were identified and the resultant optimum values of flank wear and surface roughness were found to be in good agreement with the results of a validation experiment under a similar condition. The optimized values showed a significant reduction in roughness and flank wear.     

The selection of milling parameters by the PSO-based neural network modeling method

During the past decade, polymer nanocomposites have emerged relatively as a new and rapidly developing class of composite materials and attracted considerable investment in research and development worldwide. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. In this work, the effect of cutting parameters (spindle speed and feed rate) and nanoclay (NC) content on machinability properties of polyamide-6/nanoclay (PA-6/NC) nanocomposites was studied by using high speed steel end mill. This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network. In this regard, advantages of the statistical experimental algorithm technique, experimental measurements artificial neural network and particle swarm optimization algorithm, are exploited in an integrated manner. The results indicate that the nanoclay content on PA-6 significantly decreases the cutting forces, but does not have a considerable effect on surface roughness. Also the obtained results for modeling cutting forces and surface roughness have shown very good training capacity of the proposed PSONN algorithm in comparison to that of a conventional neural network.     

Specific cutting force coefficients modeling of end milling by neural network

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging forces of cutting tests. In this paper the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in the learning stage as the omitted number of experimental data increase the average errors increase as well.     

切削参数对车削2A14锻铝合金表面粗糙度影响的试验研究

通过正交试验得到车削2A14锻铝合金时的表面粗糙度Ra值;利用方差分析的方法研究了切削深度、主轴转速和进给量三个独立因素对Ra值影响的主次顺序,并获得最佳的切削参数组合。结果表明,进给量对Ra值的影响最大,其次是主轴转速,切削深度影响最小。     

A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6)

The aim of this work is to develop a study of Taguchi optimization method for low surface roughness value in terms of cutting parameters when face milling of the cobalt-based alloy (stellite 6) material. The milling parameters evaluated are feed rate, cutting speed and depth of cut, a series of milling experiments are performed to measure the surface roughness data. The settings of face milling parameters were determined by using Taguchi experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the milling parameters on surface roughness. Confirmation tests with the optimal levels of cutting parameters are carried out in order to illustrate the effectiveness of Taguchi optimization method. It is thus shown that the Taguchi method is very suitable to solve the surface quality problem occurring the face milling of stellite 6 material.     

Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis

This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.     

A study of surface roughness in drilling using mathematical analysis and neural networks

Drilling is one of the most common and fundamental machining processes. It is most frequently performed in material removal and is used as a preliminary step for many operations, such as reaming, tapping and boring. Because of their importance in nearly all production operations, twist drills have been the subject of numerous investigations. The aim of this study is to identify suitable parameters for the prediction of surface roughness. Back propagation neural networks are used for the detection of surface roughness. Drill diameter, cutting speed, feed and machining time are given as inputs to the neural network structure and surface roughness was estimated. Drilling experiments with 12 mm drills are performed at three cutting speeds and feeds. The number of neurons are selected from 1,2,3, ..., 20. The learning rate was selected as 0.01, and no smoothing factor was used. The best structure of neural network was selected based on a criteria including the minimum of sum of squares with the actual value of surface roughness. For mathematical analysis, an inverse coefficient matrix method was used for calculating the estimated values of surface roughness. Comparative analysis was performed between actual values and estimated values obtained by mathematical analysis and neural network structures.     

弧面凸轮高速铣削的试验研究

在五轴联动加工中心上进行了弧面凸轮的高速铣削试验,研究了切削用量对弧面凸轮轮廓表面粗糙度的影响规律。结果表明:提高切削速度、减小切削深度有利于减小轮廓表面粗糙度值;当每齿进给量f<0.08mm时,其对表面粗糙度的影响不明显。     

An investigation of the machinability of PA 6/nano-CaCO3 composite

Nowadays, polymer nanocomposites have attracted manufacturers’ attention because of their good mechanical, thermal, and physical properties. Over the past decade, the requirement of the direct machining of polymer nanocomposites has increased due to the production of most polymer nanocomposites using the extrusion method in simple cross-section and the increased demand for personalized products. In this paper, the effect of milling parameters (spindle speed and feed per tooth) and nano-CaCO₃ content on the machinability properties of PA 6/nano-CaCO₃ composites was studied by analyzing variance. Harmony search-based neural network (HSNN) was then utilized to create predictive models of surface roughness and total cutting forces from the experimental data. The results revealed that the nano-CaCO₃ content on PA 6 decreased the cutting forces significantly, but did not have a significant effect on surface roughness. Moreover, the results for modeling total cutting forces and surface roughness showed that HSNN is effective, reliable, and authoritative in modeling the surface roughness formation and total cutting force mechanism for end-milling of PA 6/nano-CaCO₃ composites.