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.     

Optimization techniques for machining operations: a retrospective research based on various mathematical models

Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.     

A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces

The objective of this paper is to develop a Taguchi optimization method for low surface roughness in terms of process parameters when milling the mold surfaces of 7075-T6 aluminum material. Considering the process parameters of feed, cutting speed, axial-radial depth of cut, and machining tolerance, a series of milling experiments were performed to measure the roughness data. A regression analysis was applied to determine the fitness of data used in the Taguchi optimization method using milling experiments based on a full factorial design. Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. A confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of mold surfaces.     

基于粗糙度形成机理的曲面高速铣精加工参数选用

为在曲面精加工中获得理想的表面粗糙度,通过分析表面粗糙度的形成机理,建立了粗糙度与走刀行距、进给率关系的数学模型;通过实验,建立了高速曲面铣削时粗糙度与加工倾角、主运动线速度关系的图谱,实现了在生产过程中按照加工目标的表面粗糙度确定相应的走刀行距、进给率、加工倾角、主运动线速度等加工参数.研究结果表明,该研究对提高加工...     

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. An experimental data set is obtained with speed, feed, depth of cut and vibration as input parameters and surface roughness as output parameter. The input-output data set is used for training and validation of the proposed techniques. After validation they are forwarded for the prediction of surface roughness. Both the hybrid techniques are found to be superior over their respective individual intelligent techniques in terms of computational speed and accuracy for the prediction of surface roughness.     

An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design

Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46–47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400?m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400?m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400?m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400?m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3?μm, while cutting speed above 1,400?m/min should be avoided even if the feed rate and axial depth of cut are low.     

涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

铝合金高速切削表面粗糙度影响因素研究

为有效降低高速切削中铝合金的表面粗糙度值,通过多因素正交试验和单因素试验对各铣削参数进行研究,结果显示:各参数对铝合金表面粗糙度影响程度从大到小的顺序是:切削深度、主轴转速、每齿进给量、行距,且转速为18000r/min,每齿进给量为0.075mm,行距和每齿进给量一致,选择较小的切削深度时,在铝合金表面可获得较好的加工质量。     

Multi-objective optimization of machining parameters considering energy consumption

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product.     

Multi-objective optimization of multi-pass face milling using particle swarm intelligence

In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.     

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.     

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.     

Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.     

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.     

Enhancement and verification of a machined surface quality for glass milling operation using CBN grinding tool—Taguchi approach

Nowadays, the demand for high product quality focuses extensive attention to the quality of machined surface. The (CNC) milling machine facilities provides a wide variety of parameters set-up, making the machining process on the glass excellent in manufacturing complicated special products compared with other machining processes. However, the application of grinding process on the CNC milling machine could be an ideal solution to improve the product quality, but adopting the right machining parameters is required. Taguchi optimization method was used to estimate optimum machining parameters with standard orthogonal array L₁₆ (4⁴) to replace the conventional trial and error method as it is time-consuming. Moreover, analyses on surface roughness and cutting force are applied which are partial determinant of the quality of surface and cutting process. These analyses are conducted using signal to noise (S/N) response analysis and the analysis of variance (Pareto ANOVA) to determine which process parameters are statistically significant. In glass milling operation, several machining parameters are considered to be significant in affecting surface roughness and cutting forces. These parameters include the lubrication pressure, spindle speed, feed rate, and depth of cut as control factors. While, the lubrication direction is considered as a noise factor in the experiments. Finally, verification tests are carried out to investigate the improvement of the optimization. The results showed an improvement of 49.02% and 26.28% in the surface roughness and cutting force performance, respectively.     

Optimization of cryogenic milling parameters for aluminum honeycomb treated by ice fixation method

This paper presents the first comprehensive investigation that aluminum honeycomb has inevitable machining defect in milling process, such as deformation, burr, and collapse. Ice fixation method was used to clamp workpieces, and inner-injection liquid nitrogen was employed for a series of cryogenic milling machining. In the machining process, the main machining parameters including in honeycomb orientation, milling width, cutting depth, cutting speed, and feed were executed experimental research. Meanwhile, the machining parameter optimization, range, and significant analysis were adopted to analyze the influence of machining parameters on the machining surface quality, as well as the optimal parameter combination and milling machining surface quality were predicted and verified. The results show that the ice fixation aluminum honeycomb method with cryogenic milling is much advanced than that of conventional ones, and many machining defects are effectively restrained. At the same time, the influence of machining parameters on machining qualities in descending order is cutting depth, cutting speed, honeycomb orientation, feed, and milling width. The minimum roughness value (Ra?=?0.356 μm) of the predicted machining surface is similar to the actual machining result (Ra?=?0.362 μm). It verifies the feasibility of the optimization method. Furthermore, it is proved that the ice fixation + liquid nitrogen cooling method has a positive effect on the high milling quality and implement efficiency for aluminum honeycomb and other difficult-to-machine materials.     

Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Empirical models for machining time and surface roughness are described for exploring optimized machining parameters in turning operation. CNC turning machine was employed to conduct experiments on brass, aluminum, copper, and mild steel. Particle swarm optimization (PSO) has been used to find the optimal machining parameters for minimizing machining time subjected to desired surface roughness. Physical constraints for both experiment and theoretical approach are cutting speed, feed, depth of cut, and surface roughness. It is observed that the machining time and surface roughness based on PSO are nearly same as that of the values obtained based on confirmation experiments; hence, it is found that PSO is capable of selecting appropriate machining parameters for turning operation.     

Surface topography and roughness of high-speed milled AlMn1Cu

The aluminum alloy AlMn1Cu has been broadly applied for functional parts production because of its good properties. But few researches about the machining mechanism and the surface roughness were reported. The high-speed milling experiments are carried out in order to improve the machining quality and reveal the machining mechanism. The typical topography features of machined surface are observed by scan electron microscope(SEM). The results show that the milled surface topography is mainly characterized by the plastic shearing deformation surface and material piling zone. The material flows plastically along the end cutting edge of the flat-end milling tool and meanwhile is extruded by the end cutting edge, resulting in that materials partly adhere to the machined surface and form the material piling zone. As the depth of cut and the feed per tooth increase, the plastic flow of materials is strengthened and the machined surface becomes rougher. However, as the cutting speed increases, the plastic flow of materials is weakened and the milled surface becomes smoother. The cutting parameters (e.g. cutting speed, feed per tooth and depth of cut) influencing the surface roughness are analyzed. It can be concluded that the roughness of the machined surface formed by the end cutting edge is less than that by the cylindrical cutting edge when a cylindrical flat-end mill tool is used for milling. The proposed research provides the typical topography features of machined surface of the anti-rust aluminum alloy AlMn1Cu in high speed milling.     

Effect of machining parameters on the milling process of 2.5D C/SiC ceramic matrix composites

Abstract

The C/SiC ceramic matrix composites are widely used for high-value components in the nuclear, aerospace and aircraft industries. The cutting mechanism of machining C/SiC ceramic matrix composites is one of the most challenging problems in composites application. Therefore, the effects of machining parameters on the machinability of milling 2.5D C/SiC ceramic matrix composites is are investigated in this article. The related milling experiments has been carried out based on the C/SiC ceramic matrix composites fixed in two different machining directions. For two different machining directions, the influences of spindle speed, feed rate and depth of cut on cutting forces and surface roughness are studied, and the chip formation mechanism is discussed further. It can be seen from the experiment results that the measured cutting forces of the machining direction B are greater than those of the in machining direction A under the same machining conditions. The machining parameters, which include spindle speed, feed rate, depth of cut and machining direction, have an important influence on the cutting force and surface roughness. This research provides an important guidance for improving the machining efficiency, controlling and optimizing the machined surface quality of C/SiC ceramic matrix composites in the milling process.     

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.