Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.     

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.     

Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches

Optimization techniques using evolutionary algorithm (EA) are becoming more popular in engineering design and manufacturing activities because of the availability and affordability of high-speed computers. In this work, an attempt was made to solve multi-objective optimization problem in turning by using multi-objective differential evolution (MODE) algorithm and non-dominated sorting genetic algorithm(NSGA-II). Optimization in turning means determination of the optimal set of machining parameters to satisfy the objectives within the operational constraints. These objectives may be minimum tool wear, maximum metal removal rate or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are cutting speed, feed rate, and depth of cut. The optimum set of these three input parameters is determined for a particular job-tool combination of EN24 steel and tungsten carbide during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate after satisfying the constraints of temperature and surface roughness. The regression models, developed for tool wear, temperature, and surface roughness were used for the problem formulation. The non-dominated solution set obtained from MODE was compared with NSGA-II using the performance metrics and reported     

数控强力车削切削用量优化的图形分析法

建立了一个使金属切除率最大为目标函数 ,刀具经济使用寿命、加工精度、机床功率和机床进给机构强度等为约束条件的数学模型。在解这个非线性规划问题时 ,提出了图形分析的优化方法 ,用该法确定数控车削加工中最佳切削用量具有准确、简捷等特点     

Influence of machining parameters on the machinability of particulate reinforced Al/SiC–MMC

The paper presents the result of an experimental investigation on the machinability of silicon carbide particulate aluminium metal matrix composite during turning using a rhombic uncoated carbide tool. The influence of machining parameters, e.g. cutting speed, feed and depth of cut on the cutting force has been investigated. The influence of the length of machining and cutting time on the tool wear and the influence of various machining parameters, e.g. cutting speed, feed, depth of cut on the surface finish criteria has been analyzed through the various graphical representations. The combined effect of cutting speed and feed on the flank wear has also been investigated. The influence of cutting speed, feed and depth of cut on the tool wears and built-up edge is analyzed graphically. The job surface condition and wear of the cutting tool edge for the different sets of experiments have been examined and compared for searching out the suitable cutting condition for effective machining performance during turning of Al/SiC-MMC. Test results show that no built-up edge is formed during machining of Al/SiC-MMC at high speed and low depth of cut. From the test results and different SEM micrographs, suitable range of cutting speed, feed and depth of cut can be selected for proper machining of Al/SiC-MMC.     

Optimization of cutting parameters in micro end milling operations in dry cutting condition using genetic algorithms

Micro end milling with fine grained carbide end mills is an economical way to machine small and medium lots of micro components. Considering the sensitivity of the slender end mills which are very costly compared to conventional end mills, it is imperative that the machining parameters are chosen appropriately so as to ensure maximum tool life and minimum possible production cost without violating any of the imposed constraints. Unlike in conventional end milling operations the tool life in micro end milling operations increases with axial depth of cut till it equals the tool diameter and this makes it even difficult to ascertain the appropriate depth of cut to machine a specific component. In this paper the influence of depth of cut on tool life is illustrated and depth of cut is also considered as one of the decision variables in the optimization problem. More over in this paper Genetic Algorithms (GA) based on natural laws of evolution is used to optimize the cutting parameters. Finally a test case is presented to give a clear picture of the application of the optimization algorithm.     

HSM-ADAPTED TOOL PATH CALCULATION FOR POCKETING

High-speed milling imposes a precise choice of cutting conditions, because the feed rate and the radial depth of cut influence the maximum forces on cutting edges. But the control of these cutting conditions for pocket machining is very difficult due to the complex tool path shape. Our work is focused on the improvement of the geometrical definition of the tool path, in order to ensure a better respect of the cutting conditions required for HSM. Initially, we study variations in the radial depth of cut and the real feed rate, when the tool follows usual tool paths for pocketing. Numerical simulations and experimental measurements are used. Next, a new tool path computation method that increases the real feed rate and respects radial depth of cut requirements is proposed. The computation takes into account both the geometrical requirements and the HSM dynamic requirements. Such tool paths reduce machining time and respect initial cutting parameters which are favorable for process reliability and tool life.     

HSM-ADAPTED TOOL PATH CALCULATION FOR POCKETING

High-speed milling imposes a precise choice of cutting conditions, because the feed rate and the radial depth of cut influence the maximum forces on cutting edges. But the control of these cutting conditions for pocket machining is very difficult due to the complex tool path shape. Our work is focused on the improvement of the geometrical definition of the tool path, in order to ensure a better respect of the cutting conditions required for HSM. Initially, we study variations in the radial depth of cut and the real feed rate, when the tool follows usual tool paths for pocketing. Numerical simulations and experimental measurements are used. Next, a new tool path computation method that increases the real feed rate and respects radial depth of cut requirements is proposed. The computation takes into account both the geometrical requirements and the HSM dynamic requirements. Such tool paths reduce machining time and respect initial cutting parameters which are favorable for process reliability and tool life.     

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 .     

Application of a variable flow stress machining theory to helical end milling

A methodology of modeling chip geometry of flat helical end milling based on a variable flow stress machining theory is presented in this article. The proposed model is concerned with the variation of the width of cut thickness. The nonuniform chip thickness geometry is discretized into several segments based on the radial depth of cut. The chip geometry for each segment is considered to be constant by taking the average value of the maximum and minimum chip thickness. The maximum chip thickness for each chip segment is computed based on the current width of cut, feed per tooth and the cutter diameter. The subsequent radial depth of cut is subtracted from the discretized size of the width of cut to obtain the minimum chip thicknesses. The forces for each segment are summed to obtain the total forces acting on the system of the workpiece and the tool. The cutting forces can be predicted from input data of work material properties, cutter configuration and the cutting conditions used. The validation of the proposed model is achieved by correlating experimental results with the predicted results obtained.     

The efficiency range of economical cutting conditions for a multistage transfer machine under a failure replacement strategy

This paper presents models for calculating the optimal cutting feed rate and spindle speed at each stage in a multistage transfer machine. The optimal cutting conditions are determined by taking into account the cutting constraints for three objective functions, which are: minimum expected cycle time, minimum expected cost per unit, and maximum expected profit rate, using a one-dimensional search procedure. The efficiency range in which the optimal solutions for the three objective functions can be found is also analyzed. In addition, the optimal cutting conditions at each stage are compared to those of a stand-alone cutting machine.     

Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach

In present work performance of coated carbide tool was investigated considering the effect of work material hardness and cutting parameters during turning of hardened AISI 4340 steel at different levels of hardness. The correlations between the cutting parameters and performance measures like cutting forces, surface roughness and tool life, were established by multiple linear regression models. The correlation coefficients found close to 0.9, showed that the developed models are reliable and could be used effectively for predicting the responses within the domain of the cutting parameters. Highly significant parameters were determined by performing an Analysis of Variance (ANOVA). Experimental observations show that higher cutting forces are required for machining harder work material. These cutting forces get affected mostly by depth of cut followed by feed. Cutting speed, feed and depth of cut having an interaction effect on surface roughness. Cutting speed followed by depth of cut become the most influencing factors on tool life; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.     

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.     

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     

Cutting parameter optimization in NC milling

The continuous demand for higher productivity and product quality asks for better optimizing of the machining process. In this case, numerical controlled (NC) milling is a processing technology massively applied in the metal manufacturing industry; it has received very important interest in this century because it has a very high productivity and high work piece surface quality. The main objective of this work is to evaluate the machining time of different cycles, in 2.5?D NC milling. The prediction of the optimal values of cutting speed was analyzed to minimize both time and cost of die production. Optimum and economical values of cutting speed give, respectively, minimum production time and minimum production cost. An experimental study is carried out to validate machining time calculation models developed in this work. The cutting parameters analyzed in this study are cutting speed, feed per tooth, and the radial cutting depth.     

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.     

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.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Multiple-response optimization for micro-endmilling process using response surface methodology

In the present trend, new fabrication methods for producing miniaturized components are gaining popularity due to the recent advancements in micro-electro mechanical systems. Micro-machining differs from the traditional machining with the small size tool, resolution of x?Cy and z stages. This paper focuses RSM for the multiple response optimization in micro-endmilling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness. In this work, second-order quadratic models were developed for MRR and surface roughness, considering the spindle speed, feed rate and depth of cut as the cutting parameters, using central composite design. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in good agreement with the predicted values.     

Experimental investigation on finish intermittent turning of UNS M11917 magnesium alloy under dry machining

An experimental investigation on finish intermittent turning of UNS M11917 magnesium alloy under dry machining is presented. The objective of the study is the analysis of the chip morphology, surface quality and temperature when varying cutting conditions. The intermittent cutting process is analysed using three different workpieces (one continuous and two discontinuous). The experimental plan is based on full factorial designs. Main results of the investigation include the identification of the feed rate as the most important parameter to explain the surface roughness, while no clear influence was found for the cutting speed and slot width. The maximum temperature measured on the tool during the tests was below 50 °C in all of the tests. These low temperature values are explained by the low machining times, depths of cut and cutting speeds used. Thus, for the range of the cutting parameters tested, finish operations can be performed using dry machining under safe conditions. In addition, it was identified that the increase of the depth of cut and feed rate led to higher maximum temperatures, while the increase of the slot width led to lower values. Finally, the morphology of the chips can be classified as segmented chips, including the arc, elemental and spiral chips. Thus, in terms of machining, these chips can be considered as favourable, but, in terms of ignition, these chips are more likely to ignite.