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.     

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.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes

Nowadays, the micrometric and nanometric dimensional precision of industrial components is a common feature of micro-milling manufacturing processes. Hence, great importance is given to such aspects as online metrology and real-time monitoring systems for accurate control of surface roughness and dimensional quality. A real-time monitoring system is proposed here to predict surface roughness with an estimation error of 9.5%, by using the vibration signal that is emitted during the milling process. In the experimental setup, the z-axis component vibration is measured using two different diameters under several cutting conditions. Then, an adaptive neuro-fuzzy inference system model is implemented for modeling surface roughness, yielding a high goodness of fit indices and a good generalization capability. Finally, the optimization process is carried out by considering two contradictory objectives: unit machining time and surface roughness. A multi-objective genetic algorithm is used to solve the optimization problem, obtaining a set of non-dominated solutions. Pareto front representation is a useful decision-making tool for operators and technicians in the micro-milling process. An example of the Pareto front utility-based approach that selects two points close to both extreme ends of the frontier is described in the paper. In the first case (point 1), machine time is of greater importance, and in the second case (point 2), importance is attached to surface roughness. In general terms, users can select different combinations, at all times moving along the Pareto front.     

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.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

An integrated evolutionary approach for modelling and optimization of laser beam cutting process

This paper presents a new integrated methodology based on evolutionary algorithms (EAs) to model and optimize the laser beam cutting process. The proposed study is divided into two parts. Firstly, genetic programming (GP) approach is used for empirical modelling of kerf width (Kw) and material removal rate (MRR) which are the important performance measures of the laser beam cutting process. GP, being an extension of the more familiar genetic algorithms, recently has evolved as a powerful optimization tool for nonlinear modelling resulting in credible and accurate models. Design of experiments is used to conduct the experiments. Four prominent variables such as pulse frequency, pulse width, cutting speed and pulse energy are taken into consideration. The developed models are used to study the effect of laser cutting parameters on the chosen process performances. As the output parameters Kw and MRR are mutually conflicting in nature, in the second part of the study, they are simultaneously optimized by using a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm II. The Pareto optimal solutions of parameter settings have been reported that provide the decision maker an elaborate picture for making the optimal decisions. The work presents a full-fledged evolutionary approach for optimization of the process.     

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.     

钛合金铣削加工参数多目标优化研究

为解决钛合金铣削加工存在的高成本、低效率问题,以生产效率最大和刀具寿命消耗率最小为目标,建立了铣削参数优化模型。提出了扩展非支配排序遗传算法（ENSGA-Ⅱ）,对种群初始化子过程进行规范化处理,保证了种群的多样性和均匀性;根据Pareto最优原理将非支配概念从目标函数空间扩展到约束空间,使得多目标多约束问题的处理更具有适应性和有效性。实例表明,该方法具有良好的寻优能力,能够获得满意的Pareto解集。借助于该方法,工艺人员可根据优化目标灵活地选择铣削参数,更好地协调生产效率、生产成本和表面质量之间的关系。     

基于模糊分析方法的钛合金插铣加工切削参数优化

合理的切削参数有利于提高刀具的使用寿命和切削效率,因此,进行切削加工过程中的切削参数优化尤为重要。本文以提高钛合金插铣加工效率,减小加工过程中的切削力为目的,对钛合金TC4进行插铣试验。基于切削试验数据,选择切削速度、切削宽度和每齿进给量作为评价因子,以材料去除率与切削力作为评价指标,运用模糊分析方法对切削参数进行综合评价,得出切削参数对综合指标的影响程度从大到小依次为:切削速度、切削宽度和每齿进给量,并实现切削参数的优化,为实际生产加工提供参考依据。     

Process modeling and toolpath optimization for five-axis ball-end milling based on tool motion analysis

In free-form surface machining, the prediction of five-axis ball-end milling forces is quite a challenge due to difficulties of determining the underformed chip thickness and engaged cutting edge. Part and tool deflections under high cutting forces may result in poor part quality. To solve these concerns, this paper presents process modeling and optimization method for five-axis milling based on tool motion analysis. The method selected for geometric stock modeling is the dexel approach, and the extracted cutter workpiece engagements are used as input to a force prediction. The cutter entry?Cexit angles and depth of cuts are found and used to calculate the instantaneous cutting forces. The process is optimized by varying the feed as the tool?Cworkpiece engagements vary along the toolpath, and the unified model provides a powerful tool for analyzing five-axis milling. The new feedrate profiles are shown to considerably reduce the machining time while avoiding process faults.     

GH4169高温合金切削仿真分析及工艺参数优化

以GH4169高温合金为切削仿真分析对象,采用田口法、信噪比分析、极差分析和交互作用分别得出切削深度对切削力影响最大,切削速度对切削温度影响最大,切削温度随着切削速度和进给量的增大而略有增大,切削力随着切削深度和进给量的增大而显著增大,切削力增大会使切削温度升高。运用多目标遗传优化算法,得出切削参数的帕累托最优解集,优化结果和仿真试验结果误差率小于5%。     

Real-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts

Cutting tool state recognition plays an important role in ensuring the quality and efficiency of NC machining of complex structural parts, and it is quite especial and challengeable for complex structural parts with single-piece or small-batch production. In order to address this issue, this paper presents a real-time recognition approach of cutting tool state based on machining features. The sensitive parameters of monitored cutting force signals for different machining features are automatically extracted, and are associated with machining features in real time. A K-Means clustering algorithm is used to automatically classify the cutting tool states based on machining features, where the sensitive parameters of the monitoring signals together with the geometric and process information of machining features are used to construct the input vector of the K-Means clustering model. The experiment results show that the accuracy of the approach is above 95% and the approach can solve the real-time recognition of cutting tool states for complex structural parts with single-piece and small-batch production.     

Fast prediction of chatter stability lobe diagram for milling process using frequency response function or modal parameters

Prediction of chatter stability is important for planning and optimization of machining process in order to improve machining efficiency and reduce machining damage. Based on the classical analytical solution of chatter stability for milling process and in-depth analysis of the impact of modal parameters on the stability lobe diagram, a straight forward procedure for fast predicting stability lobe diagram directly using modal parameters of machining system was put forward. In consideration of the fact that the modal parameters of milling system can be estimated directly from the frequency response function using single DOF modal parameter estimation method, stability lobe diagram can be plotted directly using the tool tip’s frequency response function. The machining performances of a machining center with three different cutting tools were evaluated and the corresponding optimized cutting conditions were determined. The correctness of the proposed method was validated by good agreement of the predicted stability lobe diagram with that using the classical analytical method, and simulation results show that its calculation speed had been improved by 2–3 orders of magnitude. As a result, the proposed method of plotting stability lobe diagram using frequency response function can be utilized as an effective tool to select chatter-free cutting conditions in shop floor applications.     

Optimization and sensitivity analysis of geometrical and process parameters to reduce vibration during end milling process

Selection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude.     

Multidimensional visualization and clustering for multiobjective optimization of artificial satellite heat pipe design

This study presents a newly developed approach for visualization of Pareto and quasi-Pareto solutions of a multiobjective design problem for the heat piping system in an artificial satellite. Given conflicting objective functions, multiobjective optimization requires both a search algorithm to find optimal solutions and a decision-making process for finalizing a design solution. This type of multiobjective optimization problem may easily induce equally optimized multple solutions such as Pareto solutions, quasi-Pareto solutions, and feasible solutions. Here, a multidimensional visualization and clustering technique is used for visualization of Pareto solutions. The proposed approach can support engineering decisions in the design of the heat piping system in artificial satellites. Design considerations for heat piping system need to simultaneously satisfy dual conditions such as thermal robustness and overall limitation of the total weight of the system. The proposed visualization and clustering technique can be a valuable design tool for the heat piping system, in which reliable decision-making has been frequently hindered by the conflicting nature of objective functions in conventional approaches.     

基于切削加工特征的车削刀具的选择

通过对车削加工特征进行分析和分类,结合对车削刀具结构的研究,确定基于切削加工特征的车削刀具选择原则和方法。建立基于切削加工特征的刀具选择车削数据库系统,实现车削刀具及其切削参数的智能选取。     

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.