Concurrent multi-objective tolerance allocation of mechanical assemblies considering alternative manufacturing process selection

Concurrent design of tolerances by considering both the manufacturing cost and quality loss of each component by alternate processes of the assemblies may ensure the manufacturability, reduce the manufacturing costs, decrease the number of fraction nonconforming (or defective rate), and shorten the production lead time. Most of the current tolerance design research does not consider the quality loss. In this paper, a novel multi-objective optimization method is proposed to enhance the operations of the non-traditional algorithms (Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) and systematically distribute the tolerances among various the components of mechanical assemblies. The problem has a multi-criterion character in which three objective functions, one constraint, and three variables are considered. The average fitness factor method and normalized weighted objective function method are used to select the best optimal solution from Pareto-optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto-optimal fronts. Two more multi-objective performance measures namely optimizer overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto-optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO algorithms are best for this problem.     

Selection of machining datum and allocation of tolerance through tolerance charting technique

Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objectives.The selection of machining datum and allocation of tolerances are critical in any machining process planning as they directly affect any setup methods/machine tools selection and machining time.This paper mainly focuses on the selection of optimum machining datums and machining tolerances simultaneously in process planning.A dynamic tolerance charting constraint scheme is developed and implemented in the optimization procedure.An optimization model is formulated for selecting machining datum and tolerances and implemented with an algorithm namely Elitist Non-Dominated Sorting Genetic Algorithm(NSGA-II).The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances.     

Mathematical modeling and process parameters optimization of ultrasonic assisted electrochemical magnetic abrasive machining

Journal of Mechanical Science and Technology - A new machining technique called ultrasonic assisted electrochemical magnetic abrasive machining integrates ultrasonic vibrations, electrochemical...     

Selection of the optimal electrochemical machining process parameters using biogeography-based optimization algorithm

Electrochemical machining (ECM) has become one of the most potential and useful non-traditional machining processes because of its capability of machining complex and intricate shapes in high-strength and heat-resistant materials. For effective utilization of the ECM process, it is often required to set its different machining parameters at their optimal levels. Various mathematical techniques have already been proposed by past researchers to determine the optimal combinations of the different machining parameters of the ECM process. In this paper, the machining parameters of an ECM process and a wire electrochemical turning process are optimized using the biogeography-based optimization (BBO) algorithm. Both the single- and multi-response optimization models are considered. The optimization performance of the BBO algorithm is also compared with that of other population-based algorithms, e.g., genetic algorithm and artificial bee colony algorithm. It is observed that the BBO algorithm outperforms the others with respect to the optimal values of different process responses and computation time.     

面向制造的机床并行公差优化设计

运用并行公差优化设计方法对精密卧式加工中心线性轴进行公差分配.在保证机床现有精度条件下,综合分析机床各线性轴制造误差的敏感度和加工成本,优化了给定结构机床线性轴导轨选型和导轨基面工序公差的精度设计,节约了机床制造成本.     

线切割加工工艺参数优化方法综述

线切割是一种非传统的材料加工方法,其主要的工艺参数有峰值电流、开路电压、脉冲宽度、脉冲间隔、伺服电压、丝速和丝的张力等,主要的工艺指标有材料去除率、表面质量、切缝宽度和白层厚度。综述了线切割工艺参数优化的研究方法,主要包括:人工神经网络、响应曲面法、田口法、灰色关联分析法和遗传算法。通过这些方法可以建立工艺参数和工艺指标之间的关系,从而发现影响工艺的重要参数,确定工艺参数的优化组合,预测基于优化参数的工艺指标。     

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-response optimization of machining parameters on red mud-based aluminum metal matrix composites in turning process

This paper presents the findings of an experimental investigation into the effects of cutting speed, feed rate, depth of cut, and nose radius in computer numerical control (CNC) turning operation performed on red mud-based aluminum metal matrix composites. This paper investigates optimization design of a turning process performed on red mud-based aluminum metal matrix composites. The major performance characteristics selected to evaluate the process are surface roughness, power consumption, and vibration, and the corresponding turning parameters are cutting speed, feed, depth of cut, and nose radius. Taguchi-based grey analysis, which uses grey relational grade as performance index, is specifically adopted to determine the optimal combination of turning parameters. The principal component analysis (PCA) is applied to evaluate the weighting values corresponding to various performance characteristics. L9 orthogonal array design has been used for conducting the experiments. The outcome of confirmation experiments reveals that grey relational analysis coupled with PCA can effectively be used to obtain the optimal combination of turning parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the turning performance of red mud-based aluminum metal matrix composites in CNC turning process.     

Neural-network-based modeling and optimization of the electro-discharge machining process

In this research, a new integrated neural-network-based approach is presented for the prediction and optimal selection of process parameters in die sinking electro-discharge machining (EDM) with a flat electrode (planing mode). A 3–6–4–2-size back-propagation neural network is developed to establish the process model. The current (I), period of pulses (T), and source voltage (V) are selected as network inputs. The material removal rate (MRR) and surface roughness (Ra) are the output parameters of the model. Experimental data were used for training and testing the network. The results indicate that the neural model can predict process performance with reasonable accuracy, under varying machining conditions. The effects of variations of the input machining parameters on process performance are then investigated and analyzed through the network model. Having established the process model, a second network, which parallelizes the augmented Lagrange multiplier (ALM) algorithm, determines the corresponding optimum machining conditions by maximizing the MRR subject to appropriate operating and prescribed Ra constraints. The optimization procedure is carried out in each level of the machining regimes, such as finishing (Ra≤2 μm), semi-finishing (Ra≤4.5 μm), and roughing (Ra≤7 μm), from which, the optimal machining parameter settings are obtained. The optimization results have also been discussed, verified experimentally, and the amounts of relative errors calculated. The errors are all in acceptable ranges, which, again, confirm the feasibility and effectiveness of the adopted approach.     

Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Electro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (C_p), peak current (I_p), pulse on time (T_on), duty cycle (DC) and gap voltage (V_g) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L₂₇ orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.     

基于资金时间价值的并行公差优化设计

总结了经典的成本公差模型,基于资金的时间价值原理对经典成本公差模型进行优化,提出了多种优化后的成本公差模型计算方法。利用优化后的成本公差模型,将制造成本、质量损失成本同时考虑到并行公差设计的目标函数中,实现了并行公差的优化设计。通过工程实例证明了所提方法的有效性,结果表明,优化后的成本公差模型更贴近实际,计算得到的总成本更为合理,有效地反应了物价上涨、通货膨胀、资金的时间价值等现实因素。     

An economic machining process model with interval parameters

The primary objective of a machining economics model is to determine the optimal cutting parameters that minimize production costs while satisfying some design constraints. When the parameters in a machining economics model have interval values, the associated problem becomes an interval machining economics problem, and the objective value will also have interval value; that is, lying in a range. This paper develops a solution method that is able to derive the interval unit production cost of a machining economic model with interval parameters. A pair of two-level machining economics problems is formulated to calculate the upper bound and lower bound of the unit production cost. Based on the duality theorem, the two-level machining economics problem is transformed into the one-level conventional geometric program. Solving the corresponding pair of geometric programs produces the interval of the unit production cost. The results indicate that the cost interval contains more information for making decisions.     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

基于混合群集智能算法的并行公差优化设计

通过对并行公差优化设计的分析,将其视为一种混合变量组合优化问题.首先给出了并行公差优化设计的数学模型,然后将其映射为一类特殊的旅行商问题--顺序多路旅行商问题,从而降低了问题的求解难度.利用蚁群优化算法和粒子群优化算法,分别在求解离散和连续变量优化时的优势,提出了一种求解并行公差优化设计问题的混合群集智能算法.通过一个计算实例,将混合群集智能算法分别与遗传算法和模拟退火算法进行了比较,结果表明,前者具有更强的搜索能力和较高的效率.同时,混合群集智能算法也为求解一般意义的混合变量优化问题提供了借鉴和参考.     

Study of machining parameters optimization for different materials in WEDM

In process planning of wire electrical discharge machining (WEDM), determination of appropriate machining conditions is likely to face problems in many ways. In addition to the construction of the relationship between machining parameters and machining characteristics, optimization search technique, a large number of experiments must be conducted repeatedly to renew parameters for different workpiece materials. The concept of specific discharge energy (SDE) was employed in this paper to represent the WEDM property of workpiece materials as one of the machining parameters. Two kinds of materials with distinctive SDE values, i.e., higher and lower, respectively, were selected for our experiments. The experimental data obtained were used, and a neural network that can accurately predict the relationship between machining parameters and machining characteristics was constructed. It was found that the predicted error was less than 7 %. The optimization technique of genetic algorithms was employed, and the optimal combination of machining parameters that meet the required machining characteristics for different workpiece materials was obtained. The system proposed in this study is both user-friendly and practical. It can save considerable time and cost during the construction of the database for the expert system of process planning.     

车削加工中切削用量的多目标优化建模

针对金属切削加工中切削用量优化的特点，在分析车削用量传统优化模型缺点的基础上，建立了车削用量多目标模糊优化模型，并根据模糊集合原理，将其转化为一个传统的单目标约束优化问题，可用任一非线性优化方法求解。为车削用量的合理选择提供了理论依据。     

复杂流程系统安全的资源配置与优化

为解决流程系统的安全保障问题,提出了基于动态规划的安全资源配置与优化方法。首先通过综合分析系统的安全性,确定评估系统安全的特征参数和参数的计算方法;然后,以流程系统固有的网络结构模型为对象,引入安全特征参数,采用动态规划逐步求精的最优原理,实现安全的资源配置与优化过程。该方法能够满足投资数量的限制和最大限度提升安全的约束条件,且能够对系统安全较为薄弱的环节优先配置资源。最后,通过对一个流程系统的分析,验证了基于动态规划的安全资源配置与优化方法的可行性。     

加工过程PID控制的优化设计

基于最优化设计方法的加工过程PID控制,以铣床加工过程为例,针对原PID控制器下系统产生超调量过大,调整时间过长,控制效果不理想等问题,借助MATLAB软件及其附带的Simulink软件,设计了基于ITAE指标的最优化PID控制器.仿真结果表明,控制器输出信号平稳,其变化范围小.加工过程在最优化控制器控制下有较好的快速性、稳定性和准确性.     

基于粒子群算法的装配公差优化分配

装配公差分配是产品公差设计的重要组成部分.目前装配公差优化分配主要使用遗传算法.为了提高收敛速度,避免早熟收敛,提出了基于粒子群算法的装配公差优化分配方法.采用了基于实数的编码表示方法,以装配公差分配的优化目标函数作为评价函数,利用罚常数将约束条件并入评价函数中.一个实例的优化结果表明所提方法的收敛性、稳定性和算法效率均优于基于遗传算法的方法.