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

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.     

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.     

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.     

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.     

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.     

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.     

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.     

An intelligent system for low-pressure die-cast process parameters optimization

Low-pressure die-cast (LPDC) is widely used in manufacturing thin-walled aluminum alloy products. Since the quality of LPDC parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improve the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the LPDC process. In this method, considering the more complicated preparation process of thin-walled casting, an ANN model combining learning vector quantization and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. Meanwhile, the orthogonal array design and numerical simulation is applied to obtain the training samples instead of carrying out a real experiment for the sake of cost saving. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component of 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared. The results indicate that the proposed intelligent system is an effective tool for the process optimization of LPDC.     

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.     

Cost optimization for the cutting a cobalt chrome refractory material

This paper shows a complete approach to solve a given problem, from the experimentation to the optimization of different cutting parameters. To solve an industrial problem of slotting CoCr29Ni10W7, a Cobalt-based refractory material, we have implemented a design of experiment to determine the effect of cutting parameters on tool life, surface roughness, and cutting forces. After theses trials, an optimization approach has been implemented to find the lowest manufacturing cost while respecting the roughness constraints and cutting force limitation constraints. The optimization approach is based on the Response Surface Method using the Sequential Quadratic programming algorithm and Kriging interpolation for a constrained problem.     

Investigations into high-speed rough and finish end-milling of hardened EN24 steel for implementation of control strategies

High-speed end-milling is used for production of variety of parts, dies, and molds made of hardened EN24 steel which are widely used in power and transport industries. Since desired productivity and quality are important in these industries, different strategies are needed for rough and finish end-milling operations. In this paper, a framework is presented for integrating different requirements of high-speed end-milling. In flat end-milling experiments, slots are machined in hardened EN24 steel using single insert cutter under different sets of cutting parameters for roughing and finishing operations. For rough end-milling, the responses such as material removal volume, tool wear and cutting forces are measured with respect to cutting time. A response surface is developed to predict material removal volume and a set of cutting parameters is selected for a given range of material removal volume using differential evolution (DE) algorithm till the tool wear reaches certain value. The experimental data is also used to develop Bayesian-based artificial neural network (ANN) model. Using this ANN model, reference values for cutting force and cutting time are generated for rough end-milling. Similarly, DE is used to predict a set of cutting parameters for a given range of surface roughness using response surface model. The reference cutting force is obtained for finish end-milling using ANN model. These reference values are useful in the monitoring and implementation of control strategy for the high-speed end-milling operations.     

基于遗传算法的超精密切削表面粗糙度预测模型参数辨识及切削用量优化

建立易于分析各切削用量对粗糙度影响关系的表面粗糙度预测模型和最优的切削用量组合,是超精密切削加工技术的不断发展的需要。针对最小二乘法和传统优化方法的不足,提出了将遗传算法用于超精密切削表面粗糙度预测模型的参数辨识,并用于求解最优切削用量,给出了金刚石刀具超精密切削铝合金的表面粗糙度预测数学模型和切削用量优化结果,进行了遗传算法和常规优化算法的比较,结果表明遗传算法较最小二乘法和传统的优化方法更适合于粗糙度预测模型的参数辨识及保证切削用量的最优。     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks

This paper focuses on optimisation of process parameters of the turning operation, using artificial intelligence techniques such as support vector regression (SVR) and artificial neural networks (ANN) integrated with genetic algorithm (GA). The model is trained using the turning parameters as the input and corresponding surface roughness, tool wear and power required as the output. Data, obtained from conducting experiments is analysed using support vector machine (SVM) and artificial neural network. SVM, a nonlinear model, is learned by linear learning machine by mapping into high-dimensional kernel-induced feature space. The genetic algorithm is integrated with these to find the optimum from the response surface generated. The results are compared with those obtained by integrating GA with traditional models like response surface methodology (RSM) and regression analysis (RA). This paper illustrates the impact that techniques based on artificial intelligence have on optimising processes.     

A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.     

陶瓷刀具高速干式切削等温淬火球铁的表面粗糙度研究

结合现有等温淬火球墨铸铁(ADI)材料的生产情况,制备三组试样并测定力学性能;采用CC650陶瓷刀具实施高速切削试验,探讨ADI高速切削时加工材料-刀具材料-切削用量-表面粗糙度之间的关系,基于微粒群算法建立了ADI高速切削过程中工件表面糙度与切削参数之间的理论模型,为高速切削加工ADI的最佳生产工艺提供理论指导。     

Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation

Our goal is to propose a useful and effective method to determine optimal machining parameters in order to minimize surface roughness, resultant cutting forces and maximize tool life in the turning process. At first, three separate neural networks were used to estimate outputs of the process by varying input machining parameters. Then, these networks were used as optimization objective functions. Moreover, the proposed algorithm, namely, GA and PSO were utilized to optimize each of the outputs, while the other outputs would also be kept in the suitable range. The obtained results showed that by using trained neural networks with genetic algorithms as optimization objective functions, a powerful model would be obtained with high accuracy to analyze the effect of each parameter on the output(s) and optimally estimate machining conditions to reach minimum machining outputs.     

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque.     

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.