Optimization of multi-pass turning using particle swarm intelligence

This paper proposes a methodology for selecting optimum machining parameters in multi-pass turning using particle swarm intelligence. Often, multi-pass turning operations are designed to satisfy several practical cutting constraints in order to achieve the overall objective, such as production cost or machining time. Compared with the standard handbook approach, computer-aided optimization procedures provide rapid and accurate solutions in selecting the cutting parameters. In this paper, a non-conventional optimization technique known as particle swarm optimization (PSO) is implemented to obtain the set of cutting parameters that minimize unit production cost subject to practical constraints. The dynamic objective function approach adopted in the paper resolves a complex, multi-constrained, nonlinear turning model into a single, unconstrained objective problem. The best solution in each generation is obtained by comparing the unit production cost and the total non-dimensional constraint violation among all of the particles. The methodology is illustrated with examples of bar turning and a component of continuous form.     

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.     

边缘分布估计算法在车削参数优化中的应用

在数控车削中,降低工件加工成本具有重要的实际意义,但如何选择合理的车削参数以达到最小化加工成本是一个多约束非线性的复杂优化问题。针对该问题,提出基于边缘分布估计的UMDArp和UMDAp算法。在接近实际加工约束条件下,同时优化粗精车削参数,选出合适的加工参数组合(粗切削速度、粗进给量、粗车量、粗车次数、精切削速度、精进给量和精车量)。同时,使用基因修复策略和惩罚函数相结合的约束处理方法,进一步提高算法寻优性能。计算机模拟表明,UMDArp算法能搜索到比以往提出的启发性算法更优的车削参数组合,从而减小加工开销。     

Machining Parameters Optimisation for Turning Cylindrical Stock into a Continuous Finished Profile Using Genetic Algorithm (GA) and Simulated Annealing (SA)

The economics of machining have been of interest to many researchers. Many researchers have dealt with the optimisation of machining parameters for turning operations with constant diameters only. All CNC machines produce finished components from bar stock. Finished profiles consist of straight turning, facing, taper and circular machining. This research concentrates on optimising the machining parameters for turning cylindrical stock into continuous finished profiles. Arriving at a finished profile from a cylindrical stock is done in two stages, rough machining and finish machining. Rough machining consists of multiple passes and finish machining consists of single-pass contouring after the stock is removed in rough machining. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost. In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost, subject to a set of practical con-straints. The constraints considered in this problem are cutting force, power constraint and tool tip temperature. Due to high complexity of this machining optimisation problem, a simulated annealing (SA) and genetic algorithm (GA) are applied to resolve the problem. The results obtained from GA and SA are compared. ID="A2"Correspondance and offprint requests to: Dr P. Asokan, Department of Production Engineering, Regional Engineering College, Tiruchirap–palli–620 015, Tamil Nadu, India. E-mail: asokan@rect.ernet.in     

双刀并行数控车削中的切削参数优化方法

为优化双刀并行车削中的切削参数,降低加工成本,提出了结合蚁群算法和子问题枚举算法的切削参数优化算法。以最小化加工成本为目标函数,以粗精车削两阶段的切削参数为决策变量,建立了双刀并行车削的切削参数优化模型;根据车削加工的特点,将参数优化问题分解成若干个子问题,并推导出相应的加工成本理论下限,从而有效降低问题的复杂度。模拟结果表明,该算法运算效率高,能快速找到优化的车削参数,从而节约加工成本。     

数控机床制造精度的优化分配方法

精度设计是机床设计中的重要一环,包含精度分析和精度分配两个互逆问题,其中精度分配是指在满足给定整体精度的基础上优化设计机床组成零部件的精度。几何误差对机床加工精度有关键性的影响,采取一种新的思路来进行精度分配。采用多体系统理论对机床误差建模,进而得到用于精度分配的模型,用线位移误差近似表示角位移误差和垂直度误差;以制造成本最低和满足加工精度为目标,利用Matlab和遗传算法优化误差参数,对机床零部件进行精度分配;最后验证表明此方法能使性能和经济性得到较好的协调。     

A new model of the antifriction effect on wiredrawing with ultrasound

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

MACHINING PARAMETERS OPTIMIZATION FOR ALUMINA BASED CERAMIC CUTTING TOOLS USING GENETIC ALGORITHM

Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialox G tool is able to machine at lower unit production cost than CC 650 tool. The various costs affecting the unit production cost are also discussed.     

Multi-objective optimization of the ecm process

Optimization of process parameters is helpful in efficient working of the process and, hence, in lowering the cost of machining. Optimization of ECM process parameters has been achieved by considering only one objective at a time from metal removal rate, geometrical accuracy, and total process cost. From a practical point of view, a solution of the ecm problem satisfying all three objectives simultaneously is highly desirable.In the proposed model, a multi-objective problem involving the ecm process is formulated producing highly nonlinearized equations. These are then linearized by regression analysis and converted into a goal programming format. Finally, the problem is solved by the partitioning algorithm.It is concluded that the tool, or cathode, remains safe at the optimal values of design variables obtained in the examples discussed. The optimal value of voltage when metal removal rate is the only objective, is found to be higher than the case when the geometrical accuracy requirement is also to be satisfied.     

Optimization and graphical representation of machining conditions in multi-pass turning operations

This paper describes a procedure to calculate the machining conditions, such as the cutting speed, feed rate and depth of cut for turning operations with minimum production cost or the maximum production rate as the objective function. The optimum number of machining passes and the depth of cut for each pass is obtained through the dynamic programming technique and optimum values of machining conditions for each pass are determined based on the objective function criteria by search method application to the feasible region. Production cost and production time values are determined for different workpiece and tool material for the same input data. In the optimization procedure, the objective functions are subject to constraints of maximum and minimum feed rates and speeds available, cutting power, tool life, deflection of work piece, axial pre-load and surface roughness. By graphical representation of the objective function and the constraints in the developed software, the effects of constraints on the objective function can be evaluated. The parameters that are assumed to be most effective in determining the optimum point can easily be changed and the revised graph can be inspected for possible improvements in the optimum value.     

On-line integration of a process planning module with production scheduling

An approach to the on-line integration of process planning and production scheduling is reported. Based on a geometric modeller, a geometric analyser and a knowledge base, the process planner generates alternative process plans and provides automatic tool selection and calculation of the appropriate machining parameters. Time and cost estimations are input to the decision-making module in the production scheduling system that produces optimal scheduling decisions as well as a complete record of the actual state of the factory resources. An information flow, designed as a relational data model, maintains the interaction between the process planning and the production scheduling systems and provides the dynamic feedback to the process planner. Specific geometry features of the blank, the finished part and the cutting tools, and geometry features-based rules are stored in the database of a developed CAPP system by using a separate CAD interface. The integrated production planning and production scheduling system and the CAPP system were validated with rotational parts machining.     

Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques

Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration .     

一种设计和工序公差计算机辅助并行设计方法的研究

提出一种计算机辅助设计公差和工序公差并行设计的数学模型，以成本公差函数作为目标函数，以装配功能要求、加工方法、加工余量、工序制造公差范围作为约束条件，并用蒙特卡洛法模拟尺寸装配、模拟退火算法用于优化求解，实现了设计公差和工序公差并行设计，缩短了设计周期。     

基于敏感度分析的机床关键性几何误差源识别方法

零部件几何误差耦合而成的机床空间误差是影响其加工精度的主要原因,如何确定各零部件几何误差对加工精度的影响程度从而经济合理地分配机床零部件的几何精度是目前机床设计所面临的一个难题。基于多体系统理论,在敏感度分析的基础上提出一种识别关键性几何误差源参数的新方法。以一台四轴精密卧式加工中心为例,基于多体系统理论构建加工中心的精度模型,并利用矩阵微分法建立四轴数控机床误差敏感度分析的数学模型,通过计算与分析误差敏感度系数,最终识别出影响机床加工精度的关键性几何误差。计算和试验分析表明,该方法可以有效地识别出对机床综合空间误差影响较大的主要零部件几何误差因素,从而为合理经济地提高机床的精度提供重要的理论依据。     

Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.     

一种基于多因素耦合的谐波减速器传动精度逆向分析方法

谐波减速器的传动误差是由零件加工及装配过程中的几何偏心和运动偏心等偏心矢量耦合而成的,具有频域性特点,并且各种单因素引起的传动误差分布概率符合瑞利分布.通过空间运动学、谐波啮合原理,建立了基于瑞利分布的传动误差多因素耦合模型,完成了置信区间可达99％的谐波减速器传动误差的预判模型.通过预判模型能够计算出各传动误差源的权...     

Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique

Enhancing the performance of manufacturing operations represents a significant goal, especially when cost savings are linked with economies of scale to be exploited. In the area of machining optimization, the selection of optimal cutting parameters subjected to a set of technological constraints plays a key role. This paper presents a novel hybrid particle swarm optimization (PSO) algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization technique consists of a PSO-based framework wherein a properly embedded simulated annealing (SA), namely an SA-based local search, aims both to enhance the PSO search mechanism and to move the PSO away from being closed within local optima. In order to handle the numerous constraints which characterize the adopted machining mathematical model, a constraint violation function integrated with a suitable objective function has been engaged. In addition, a twofold strategy has been implemented to manage the equality constraint between the provided total depth of cut and the number of passes to be performed. Firstly, an accurate problem encoding involving only five cutting parameters has been performed. Secondly, a proper repair procedure that should be run just before any solution evaluation has been engaged. Five different test cases based on the multi-pass turning of a bar stock have been used for comparing the performance of the proposed technique with other existing methods.     

A capacitated production planning problem for closed-loop supply chain with remanufacturing

This paper addresses a dynamic capacitated production planning problem in steel enterprise employing a closed-loop supply chain strategy, in which the remanufacturing process is applied. Particularly, the remanufacturing problem considered in this paper is obviously different from the typical lot-sizing problems, within which all demands are met by production or remanufacturing without backlogs; the production, inventory, and remanufacturing levels all have limits; both the production and remanufacturing setup cost functions are arbitrary and time-varying; the objective is to minimize the total cost. Firstly, the closed-loop supply chain with remanufacturing model is formulated. Then, the genetic algorithm heuristic approaches are proposed to solve the NP-hard problem. Finally, a computational experiment is presented which can solve the 200 size problem efficiently. Furthermore, the comparisons against the branch and bound method show the effectiveness and efficiency of the proposed approach for solving the large size remanufacturing problem.     

多工序多工步制造系统切削用量的优化研究

通过对建立的多工序多工步制造系统生产时间和生产成本的数学模型的优化分析，并考虑了相应的生产条件约束限制，从而提出了基于最大生产率多工序多工步有约束制造模型切削用量优化求解思想——主目标生产效率最大，次目标生产费用最小，并给出了有效的优化算法。