Multi-objective optimization of combustion,performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO _x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO _x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO _x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.     

Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process

Hybrid machining processes (HMPs), having potential for machining of difficult to machine materials but the complexity and high manufacturing cost, always need to optimize the process parameters. Our objective was to optimize the process parameters of electrical discharge diamond face grinding (EDDFG), considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were performed on a high speed steel (HSS) workpiece at a self developed face grinding setup on an EDM machine. All the experimental results were used to develop the mathematical model using response surface methodology (RSM). The developed model was used to generate the initial population for a genetic algorithm (GA) during optimization, non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the process parameters of EDDFG process. Finally, optimal solutions obtained from pareto front are presented and compared with experimental data.     

Multi-objective optimization of oblique turning operations using finite element model and genetic algorithm

Multi-objective optimization of oblique turning operations while machining AISI H13 tool steel has been carried out using developed finite element (FE) model and multi-objective genetic algorithm (MOGA-II). The turning operation is optimized in terms of cutting force and temperature with constraints on required material removal rate and cutting power. The developed FE model is capable to simulate cutting forces, temperature and stress distributions, and chip morphology. The tool is modeled as a rigid body, whereas the workpiece is considered as elastic–thermoplastic with strain rate sensitivity and thermal softening effect. The effects of cutting speed, feed rate, rake angle, and inclination angle are modeled and compared with experimental findings. FE model is run with different parameters with central composite design used to develop a response surface model (RSM). The developed RSM is used as a solver for the MOGA-II. The optimal processing parameters are validated using FE model and experiments.     

基于非支配排序遗传算法的变速器多目标优化设计

以某新型货车变速器为研究对象,基于多目标遗传算法和有限元法,建立其多目标优化模型,包括优化数学模型和参数化的有限元模型,以多学科优化软件iSIGHT为平台,集成有限元软件ANSYS,使用基于Pareto最优概念的非支配排序遗传算法,寻求变速器在最小体积、最大传动总重合度和最小中心距目标下的最优结构设计方案,并获得了问题的Pareto最优解集.文中提出的分析方法能够有效地解决复杂结构的多参数多目标优化问题.     

数控车床切削参数优化建模研究

为提高加工效率和质量、降低加工成本,根据数控车床在粗、精加工过程中不同的切削特点和要求,分别对两种情况下的切削参数优化数学模型进行研究,并详细分析了相关的优化约束规则,最后对约束的具体实现方法做了说明。     

基于遗传算法的车削用量优化研究

为合理地选择切削用量,建立了基于遗传算法的切削用量优化系统框架结构,系统地研究了遗传算法及其相关内容。在约束、单优化目标函数数学建模的基础上,采用线性加权法建立的多目标优化函数;采用罚函数法改进目标函数,使约束直接表示在目标函数中,简化了切削用量的寻优过程。最后,应用遗传算法开发了一个车削用量优化器,对多约束条件下的切削用量优化结果进行了分析,总结了寻优过程中约束条件影响切削用量优化结果的规律。     

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Limited by techniques, the process of remanufacturing exists masses of uncertainties which have a great impact on the remanufactured parts quality, how to achieve a higher quality of mechanical products by using limited remanufactured parts precision, has become one of the key issues of remanufacturing industry. Firstly, with a target to reduce uncertainties and improve the quality of automatic products, a method of tolerance grading allocation for remanufactured parts is proposed based on the uncertainty analysis of the remanufacturing assembly. The dimensional tolerances of the mechanical parts are divided into positive and negative two groups. We use selective assembly method to reduce assembling deviation. Then, the method is proven by mathematical formulas that the remanufactured parts variance can be expanded to two times, and the tolerances can be liberalized 40 % through tolerance grading allocation method. It is also the theoretical basis for improving the reuse radio and quantitatively describing the tolerance liberalization in this paper. Finally, feasibility research on this method is studied from the angle of cost–benefit. Furthermore, a tolerance grading allocation example of remanufactured engine piston assembly in a power corporation shows the validity and practicality of the proposed method.     

Multicriteria optimization of cutting parameters in turning of UD-GFRP materials considering sensitivity

In this paper, a new multicriteria optimization approach is proposed for the selection of the optimal values of cutting conditions in machining. This approach aims to handle the possible manufacturing errors in design stage. These errors are taken into consideration as change in design parameters and the design most robust to change is selected as the optimum design. Machining of a glass fiber composite material is chosen in case studies. Experiments on the unidirectional glass fiber reinforced composite material are performed to investigate the effect of cutting speed, feed, and cutting depth on the cutting forces. Also, material removal rate values are obtained. Minimizing cutting forces and maximizing the material removal are considered as objectives. It is believed that the used method provides a robust way of looking at the optimum parameter selection problems.     

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.     

航空机匣工件车削加工变形预测及切削参数优化研究

以航空机匣工件某车削工序为对象,结合切削过程力学仿真及有限元分析方法,实现了其车削加工过程中加工变形量的预测。然后,以背吃刀量为设计变量,以加工效率为目标函数、以加工变形量为约束条件进行了车削加工切削参数优化研究,采用整数规划优化方法,通过改变走刀次数及合理分配每次走刀的背吃刀量,减少了加工变形量、提高了切削效率,实现了航空机匣工件的高效切削。     

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.     

Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.     

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the genetic algorithm technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology.     

Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method

Precision forging of the helical gear is a complex metal forming process under coupled effects with multi-factors. The various process parameters such as deformation temperature, punch velocity and friction conditions affect the forming process differently, thus the optimization design of process parameters is necessary to obtain a good product. In this paper, an optimization method for the helical gear precision forging is proposed based on the finite element method (FEM) and Taguchi method with multi-objective design. The maximum forging force and the die-fill quality are considered as the optimal objectives. The optimal parameters combination is obtained through S/N analysis and the analysis of variance (ANOVA). It is shown that, for helical gears precision forging, the most significant parameters affecting the maximum forging force and the die-fill quality are deformation temperature and friction coefficient. The verified experimental result agrees with the predictive value well, which demonstrates the effectiveness of the proposed optimization method.     

智能注射成形中工艺参数的多目标自学习优化

注射成形工艺参数是保障产品质量的关键因素。传统试错法严重依赖工艺人员的试模经验,随着注射成形工艺广泛应用于电子、航空航天等国家战略领域,产品的高端化对工艺参数智能化设置水平提出更高的要求。由于成形产品存在多方面的质量要求,且不同质量指标间可能相互制约,因此亟需一种工艺参数多目标智能优化方法,以获得不同优化目标间的帕累托最优。已有学者利用智能优化方法,如非支配排序遗传算法等,对多目标优化问题进行求解,但是此类方法需大量样本数据对质量-参数关系进行建模,存在试验次数多、且对不同材料及模具的适应性较差等问题。为解决上述问题,提出一种注射成形工艺参数多目标自学习优化方法,在优化过程中实时计算并更新各个工艺参数的梯度,并由不同质量指标的多梯度下降算法对多个目标函数进行优化,在优化过程中实现各工艺参数对产品质量影响程度的自主学习,省去了采集大量数据来建立多个质量模型的过程,实现了注射成形工艺参数的高效智能优化。在基准测试函数实验中,所提方法的优化结果与理论解的相对误差小于2%。同时数值仿真与注射成形实验结果表明,所提方法能高效获得多个优化目标的帕累托最优。     

基于自适应遗传算法的数控铣削过程参数优化仿真

基于自适应遗传算法思想建立了数控铣削用量多目标决策智能优化的数学模型，并运用遗传优化方法对铣削加工实例进行优化，实例验证表明，具有自适应交叉率和变异率的遗传算法在收敛速度和获得全局最优解的概率两个方面都有很大提高，并在数控加工动态仿真系统上验证了铣削用量多目标智能优化的数学模型的正确性和实用性。