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.     

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.     

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。     

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.     

基于遗传算法的高速铣削工艺参数优化研究

在高速铣削加工中,考虑机床和工件加工的实际约束条件,为合理选择高速铣削工艺参数,建立了最大生产率和最低加工成本的优化目标数学模型。以铣削速度、进给量、铣削深度、铣削宽度为工艺参数的优化变量,提出了基于遗传算法的高速铣削工艺参数优化方法,为高速铣削加工提供了理论依据。     

Deformation behaviour of sintered silicon–copper steel preforms of various aspect ratios during cold upsetting

This paper presents the optimization of the face milling process of 7075 aluminum alloy by using the gray relational analysis for both cooling techniques of conventional cooling and minimum quantity lubrication (MQL), considering the performance characteristics such as surface roughness and material removal rate. Experiments were performed under different cutting conditions, such as spindle speed, feed rate, cooling technique, and cutting tool material. The cutting fluid in MQL machining was supplied to the interface of work piece and cutting tool as pulverize. An orthogonal array was used for the experimental design. Optimum machining parameters were determined by the gray relational grade obtained from the gray relational analysis.     

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.     

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 .     

Intelligent parameter identification of machining Ti64 alloy

In this paper, a methodology approach based on analysis of multidimensional Pareto front is proposed. A new optimization approach helps the user to set the optimal parameters of a machining process. Four neural networks are used to model desire output responses, and they are used as objective functions. Particle swarm optimization (PSO) is used to find the best parameters that improve process. As application of approached proposed, an analysis of a multidimensional Pareto front is made considering a minimization of time, temperature, vibration, and surface roughness in a milling process of Ti64 alloy. Physical parameters for experimental approach are tool diameter, number of cutting edge of the tool, cutting speed, feed, and depth of cut. Analysing the 2D and 3D multidimensional Pareto front is generated a user table 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.     

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.     

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.     

Selection of optimal set of cutting tools for machining of polygonal pockets with islands

Selection of the optimal set of cutting tools is one of the most important steps in process planning for 2.5-D pocket machining. Conventional CAM software requires considerable input from the user in terms of selection of tool sizes and machining strategy. This trial-and-error procedure to determine the optimal process sequence tends to generate conservative and suboptimal results. This paper presents a methodology for optimal selection of a sequence of tools to minimize the total time required to end mill a non-convex polygonal pocket with or without islands using the staircase milling strategy. The algorithm decomposes the pocket geometry into convex regions and mills each region independently by selecting a sequence of tools based on the accessibility of various tools to the region. Strategies have been developed for machining the main pass and the subsequent leftover areas in order to obtain the final pocket geometry. Subsequently, the machining times for each decomposed area are aggregated while accounting for the need to use multiple passes, non-cutting time, and the tool change time. A dynamic programming approach is used to determine the optimal set of tools which minimizes the total processing time. The effect of varying the non-cutting speed and tool change time on the tool path length and number of tool selection is studied.     

Chatter prediction based on frequency domain solution in CNC pocket milling

Chatter has been a problem in CNC machining process especially during pocket milling process using an end mill with low stiffness. Since an iterative time-domain chatter solution consumes a computing time along tool paths, a fast chatter prediction algorithm for pocket milling process is required by machine shop-floor for detecting chatter prior to real machining process. This paper proposes the systematic solution based on integration of a stability law in frequency domain with geometric information of material removal for a given set of tool paths. The change of immersion angle and spindle speed determines the variation of the stable cutting depth along cornering cut path. This proposed solution transforms the milling stability theory toward the practical methodology for the stability prediction over the NC pocket milling.     

基于Pareto遗传算法的螺旋铣加工参数优化

螺旋铣是主要针对航空领域中难加工材料的先进制孔工艺技术。在螺旋铣孔过程中,主轴转速、每齿进给量和每转轴向切削深度是3个最主要的加工参数。以材料去除量和刀具耐用度为优化目标,基于Pareto多目标遗传算法,针对螺旋铣削钛合金材料在稳定性切削条件下的切削参数进行了优化,主要考虑铣削参数对孔表面质量的影响。最终通过切削实验进行了验证。     

基于动力学不确定性的重型切削工艺参数优化

针对数控重型切削加工过程的切削稳定性具有不确定性的特点,提出了在切削稳定性和机床工作能力的约束下,获得最大材料去除率的工艺参数优化方法。根据重型切削加工的工艺特点建立三维动力学模型,以机床的固有频率、阻尼比、刚度和切削力系数作为不确定因素,结合排零定理和边理论对其进行不确定性分析,获得稳健的切削稳定性叶瓣图,结合切削深度、刀具直径和刀具齿数的关系,为加工过程选择能获得最大切削深度的刀具。在此基础上,建立工艺参数优化模型,选择最佳的轴向切削深度、径向切削深度和主轴转速的组合,最后以一台加工中心上某型号发动机缸体表面的粗加工过程为例进行了验证。     

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.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.     

高速铣合金铸铁实验结果的稳健设计优化分析

针对高速切削新型合金铸铁类难加工材料时,因刀具磨损严重而导致刀具成本高的问题,采用成本较低的硬质合金刀具对Cr15Mo工件进行了铣削实验,研究了切削参数对切削力和表面粗糙度的影响,获得了可达到磨削加工效果（Ra=0.4 μm）的最佳参数组合,即切削速度vc=800 m/min,轴向切削深度ap=0.4 mm和进给量f=0.6 mm/r。基于稳健设计优化原理对实验结果进行了理论分析,研究结果表明：理论分析结果与实验结果具有很好的一致性,为同时实现高速、高质量和低成本加工的多目标参数优化方法提供了一种有效的途径。     

Effect of hardening on the machinability of C22 steel under drilling operations using twist HSS drill bit

For metallic or composite materials, the judicious choice of cutting conditions depends on several factors that may be of such objectives (time, cost of production, material removal rate, etc.) or constraints (cutting force, temperature in the machining area, consumed power, etc.). The quality of the results depends on the optimization method and the efficiency of the algorithm involved. In this paper, graphical and particle swarm optimization (PSO) methods are proposed. They aim to determine the optimal cutting conditions (cutting speed and feed per tooth) in slotting of multidirectional carbon fiber reinforced plastic laminate (CFRP), referenced G803/914, with three knurled tools having different geometries. The experiences that led to the measures of roughness, temperature, cutting efforts, and consumed power are made in the same working conditions with cutting speed ranging from 80 to 200 m/min and feed per tooth from 0.008 to 0.060 mm/rev/tooth. The results illustrate that for the graphical method, the optimum cutting speed depends on the performance “maximum total removal rate” and is the same for all the studied knurled tools while optimum feed per tooth depends on the “roughness” performance: its value depends on the tool geometry. For the PSO technique, optimum cutting speed and feed per tooth values are variable and depend on the tool geometry.