Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect

A new genetic algorithms-based method is applied for the optimization of cutting conditions and the selection of cutting tools in multi-pass turning operations. A new methodology for the allocation of total depth of cut in multi-pass turning operations is also developed. A comprehensive optimization criterion for multi-pass turning operations is developed and used as the objective function integrating the contributing effects of all major machining performance measures in all passes. The effect of progressive tool wear in optimization processes for multi-pass turning operations is included. Presented case studies demonstrate the application of the new methodology for optimal allocation of total depth of cut as well as optimization of cutting conditions and the selection of cutting tool inserts, and offer a comparison between optimization processes with and without the effect of tool wear in all passes.     

A generalized multi-pass machining model for machining parameter selection in turning

A machining parameter selection model has been developed using multi-pass turning operation as a general type. An approximation optimization solution approach has been defined for this machining model. This model is a constrained model with the objective of achieving minimum cost. Depth-of-cut and number-of-pass are considered as variables in the model. All parameters, including the number-of-passes, cutting speed, feed rate and depth-of-cut for each pass, reach optimal simultaneously in one stage. The performance of this machining parameter optimization model is examined using a example, and the results are compared with current publications that provide same input data.     

Optimization of machining conditions with practical constraints

This paper presents a model for the optimization of machining conditions in a multi-pass turning operation. Both rough cutting and finishing cutting are considered in the model and dual optimization of cost functions for each subproblem is pursued. The preventive tool replacement strategy used in practice is incorporated. Machining idle time is also regarded as a variable. After practical constraints are established, optimization is carried out using the dynamic programming method. An example illustrates the formulation of the problem and the optimization procedure     

Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

Heuristic optimization system for the determination of index positions on CNC magazines with the consideration of cutting tool duplications

This paper presents a meta-heuristic optimization system developed for the determination of optimal index positions of cutting tools on the tool magazines (automatic tool changer (ATC) or turret magazine, etc.) of CNC (computerized numerical control) machine tools. The selection of index positions is performed using a simulated annealing (SA) algorithm that takes the following as the input: (1) a list of cutting tools assigned to certain machining operations; (2) the number of copies of each cutting tool available in the workshop; (3) total number of index positions on the tool magazines; (4) the indexing time of a tool magazine of a CNC machine tool specified by manufacturer. Then, the SA algorithm determines index locations of cutting tools on the tool magazines. Dereli et al. and Dereli and Filiz previously studied the present problem by using genetic algorithms (GAs). However, the duplication of cutting tools was not taken into account in their works, although it can reduce the total tool-indexing time and therefore improve the productivity considerably. Nevertheless, the consideration of the ‘tool duplications’ makes the problem much harder to model and to solve. In this paper, a novel solution representation-scheme based on the SA, which enables easy manipulation during feasible neighbourhood solution generation, is proposed for the determination of the index positions of cutting tools on the CNC magazines with the consideration of ‘tool duplications’. Example problems are solved to present the implementation and merits of the proposed optimisation system. It is shown that it is possible to allocate the cutting tools in an efficient manner on the CNC magazines with the developed system.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

Experimental investigation of cutting parameters in machining of 100Cr6 with tangential turn-milling method

The turn-milling methods for machining operation have been developed to increase efficiency of conventional machines recently. These methods are used especially by coupling some apparatuses on the computer numerical control (CNC) machine to decrease the production time and machine costs, ensure the maximum production and increase the quality of machining. In this study, 100Cr6 bearing steel extensively used in industry has been machined by tangential turn-milling method. This paper presents an approach for optimization of the effects of the cutting parameters including cutter speed, workpiece speed, axial feed rate, and depth of cut on the surface roughness in the machining of 100Cr6 steel with tangential turn-milling method by using genetic algorithm (GA). Tangential turning-milling method has been determined to have optimum effects of cutting parameters on the machining of 100Cr6 steel. The experimental results show that the surface roughness quality is close to that of grinding process.     

Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel

Abstract

The turning process is one of the fundamental machining operations wherein optimization of parameters leads to better machining performance. This study has applied integrated Taguchi and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to determine the optimum process parameters in turning operation of EN25 steel using coated carbide tools. The process parameters considered are cutting speed, feed rate, and depth of cut. The objective is to minimize circularity and cylindricity simultaneously. An orthogonal array, Signal to Noise (S/N) ratio, and TOPSIS are employed to analyze the effects of input parameters on the output parameters. In this study, a decision matrix is formed using S/N ratios; then the TOPSIS method is used to transmogrify a multi-criteria optimization problem into a single-criterion problem. The result revealed that the proposed method is appropriate for solving multi-criteria optimization of process parameters. Results also showed that cutting speed of 215 m/min, feed rate of 0.07 mm/rev, and depth of cut of 1.5 mm are the optimum combination of process parameters.     

An inverse heat transfer problem for optimization of the thermal process in machining

It is evident that machining process causes development of large quantities of thermal energy within a relatively narrow area of the cutting zone. The generated thermal energy and the problems of its evacuation from the cutting zone account for high temperatures in machining. These increased temperatures exert a pronounced negative effect on the tool and workpiece. This paper takes a different approach towards identification of the thermal process in machining, using inverse heat transfer problem. Inverse heat transfer method allows the closest possible experimental and analytical approximation of thermal state for a machining process. Based on a temperature measured at any point within a workpiece, inverse method allows determination of a complete temperature field in the cutting zone as well as the heat flux distribution on the tool/workpiece interface. By knowing the heat flux function, one defines criterium and method of optimization, the inverse heat transfer problem transforms into extreme case. Now, the task of optimization is to determine most favourable ratio between heat flux parameters in order to preserve exploitation properties of the tool and workpiece.     

Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining

In addition to the cutting conditions, the surface quality is also affected significantly by a worn tool in machining processes. Identification of the desirable tool life so that the surface quality is maintained within a desirable level is an essential task, especially in the machining of hard materials. In this paper, an optimal tool life and surface quality were identified in the turning operation of Inconel 718 Superalloy by means of experimental investigations and intelligent methods. First, the effect of machining time (MT) at the different cutting parameters was widely investigated on the surface roughness using the neural network model. Then, the modified Non-dominated Sorting Genetic Algorithm (NSGA) was implemented to optimize tool life and surface roughness. For this purpose, a new approach was implemented and the MT was taken into account as the input and output parameters during the optimization. Finally, the results of optimization were classified and the suitable states of the machining outputs were found. The results indicate that the implemented strategy in this paper provides an efficient approach to determine a desirable criterion for tool life estimation in machining processes.     

Machining Parameter Optimization in High-Speed Milling for Inconel 718 Curved Surface

Machining technology for nickel-based alloy Inconel 718 is a hotspot and difficult problem in industrial fields and the high-speed milling (HSM) shows obvious superiority in difficult-to-process material machining. As the machining parameters are crucial in processing of Inconel 718 and the study of chip is important in metal cutting, there is an urgent need for deep research into the machining parameter optimization based on chip variation in HSM for Inconel 718 curved surface, so as to further increase the productivity of Inconel 718 in aerospace field. Regarding Inconel 718 curved surface, an experimental study about the machining parameter optimization based on chip variation in HSM is conducted. The relationship between chip shape and machining parameters is studied, and the roughness is measured and discussed for the machined curved surface. Results indicate that the chip area relates to geometric feature of curved surface, the optimal range for spindle speed is from 9000 to 11000 rpm based on chip variation, the feed per tooth should be large in case that condition permitted, and the cutting depth can be selected according to other constraint conditions. This study is significant for improving the machining quality and efficiency of Inconel 718 curved surface.     

Computer-aided optimization of multi-pass turning operations for continuous forms on CNC lathes

The Computer Numerical Control (CNC) machine is one of the most effective production facilities used in manufacturing industry. Determining the optimal machining parameters is essential in the machining process planning since the machining parameters significantly affect production cost and quality of machined parts. Previous studies involving machining optimization of turning operations concentrated primarily on developing machining models for bar components. Machined parts on the CNC lathes, however, typically have continuous forms. In this study, we formulate an optimization model for turned parts with continuous forms. Also, a stochastic optimization method based on the simulated annealing algorithm and the pattern search is applied to solve this machining optimization problem. Finally, the applications of the developed machining model and the proposed optimization algorithm are established through the numerical examples.     

Optimization of Machining Characteristics for Al／SiCp Composites using ANN／GA

The present work is focused on optimization of machining characteristics of Al/SiCp composites.The machining characteristics such as specific energy,tool wear and surface roughness were studied.The parameters such as volume fraction of SiC,cutting speed and feed rate were considered.Artificial neural networks(NN) was used to train and simulate the experimental data.Genetic algorithms(GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics .Validation of optimized results was also performed by confirmation experiments.     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

Multiple Performance Optimization of Machining Parameters on the Machining of GFRP Composites Using Carbide (K10) Tool

The present investigation focuses on the multiple performance machining characteristics of GFRP composites produced through filament winding. Grey relational analysis was used for the optimization of the machining parameters on machining GFRP composites using carbide (K10) tool. According to the Taguchi quality concept, a L27, 3-level orthogonal array was chosen for the experiments. The machining parameters namely work piece fiber orientation, cutting speed, feed rate, depth of cut and machining time have been optimized based on the multiple performance characteristics including material removal rate, tool wear, surface roughness and specific cutting pressure. Experimental results have shown that machining performance in the composite machining process can be improved effectively by using this approach.     

Studies on Machining Parameters While Milling Particle Reinforced Hybrid (Al6061/Al2O3/Gr) MMC

In this article, response surface methodology has been used for finding the optimal machining parameters values for cutting force, surface roughness, and tool wear while milling aluminum hybrid composites. In order to perform the experiment, various machining parameters such as feed, cutting speed, depth of cut, and weight (wt) fraction of alumina (Al₂O₃) were planned based on face-centered, central composite design. Stir casting method is used to fabricate the composites with various wt fractions (5%, 10%, and 15%) of Al₂O₃. The multiple regression analysis is used to develop mathematical models, and the models are tested using analysis of variance (ANOVA). Evaluation on the effects and interactions of the machining parameters on the cutting force, surface roughness, and tool wear was carried out using ANOVA. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. The optimum machining parameters obtained from the experimental results showed that lower cutting force, surface roughness, and tool wear can be obtained by employing the combination of higher cutting speed, low feed, lower depth of cut, and higher wt fraction of alumina when face milling hybrid composites using polycrystalline diamond insert.     

Optimizing cutting parameters in process planning of prismatic parts by using genetic algorithms

The determination of optimal cutting parameters, such as the number of passes, depth of cut for each pass, cutting speed and feed, which are applicable for assigned cutting tools, is one of the vital modules in process planning of metal parts, since the economy of machining operations plays an important role in increasing productivity and competitiveness. The present paper introduces a 'system software' developed to optimize the cutting parameters for prismatic parts. The system is mainly based on a powerful artificial intelligence (AI) tool, called genetic algorithms (GAs). It is implemented using C programming language and on a PC. It can be used as standalone system or as the integrated module of a process planning system called OPPS-PRI (Optimized Process Planning System for PRIsmatic parts) that was also developed for prismatic parts and implemented on a vertical machining centre (VMC). With the use of GAs, the impact and power of AI techniques have been reflected on the performance of the optimization system. The methodology of the developed optimization system is illustrated with practical examples throughout the paper.     

Influence of process parameters on cutting force and torque during drilling of glass–fiber polyester reinforced composites

This research outlines the Taguchi optimization methodology, which is applied to optimize cutting parameters in drilling of glass fiber reinforced composite (GFRC) material. Analysis of variance (ANOVA) is used to study the effect of process parameters on machining process. This procedure eliminates the need for repeated experiments, time and conserves the material by the conventional procedure. The drilling parameters and specimen parameters evaluated are speed, feed rate, drill size and specimen thickness. A series of experiments are conducted using TRIAC VMC CNC machining center to relate the cutting parameters and material parameters on the cutting thrust and torque. The measured results were collected and analyzed with the help of the commercial software package MINITAB14. An orthogonal array, signal-to-noise ratio are employed to analyze the influence of these parameters on cutting force and torque during drilling. The method could be useful in predicting thrust and torque parameters as a function of cutting parameters and specimen parameters. The main objective is to find the important factors and combination of factors influence the machining process to achieve low cutting low cutting thrust and torque. From the analysis of the Taguchi method indicates that among the all-significant parameters, speed and drill size are more significant influence on cutting thrust than the specimen thickness and the feed rate. Study of response table indicates that the specimen thickness, and drill size are the significant parameters of torque. From the interaction among process parameters, thickness and drill size together is more dominant factor than any other combination for the torque characteristic.     

Machining parameters optimization for satisfying the multiple objectives in machining of MMCs

The metal matrix composites (MMCs) have gained acceptance in an extensive range of applications owing to their high strength to mass ratio. Machining of such complex MMCs is often challenging. It is essential to optimize the controllable machining parameters to simultaneously attain manifold objectives. In the current work, response surface design is created for experiments, and Genetic algorithm (GA) combined with Principal Components Analysis (PCA) coupled Grey Relational Analysis (GRA) is employed to improve the straight turning process of MMCs. The procedure is demonstrated by machining aluminum-based MMC with 25% SiC particulates. The procedure aims at identifying optimal combination of machining parameters to obtain high surface quality at lower cutting force without increasing the specific power consumption. PCA is helpful in providing the individual uncorrelated quality characteristics called as quality indices that do not have any influence on other responses. Individual quality indices have been utilized to obtain the grey relational grade through GRA. GRA has been used to alter manifold quality indices into singular column of grey relational grade as a means to change the manifold objective problem into a sole objective problem. Then, GA has been used to get the optimal parameters combination. The novelty present in this work is the avoidance of correlation existing among the quality characteristics and combining of the GRA and GA. This is an endeavor to augment the performance and accuracy of GA to solve the optimization problem associated with the turning operation.     

基于VERICUT的炮管膛线电解加工阴极建模和数控仿真研究

渐速膛线电解加工的阴极需要通过实验反复修正,试验难度大,研制周期长。根据加工对象的几何参数和弹道方程建立数学模型,在Pro/E中建立阴极工作齿、炮管毛坯的几何模型并导入VERICUT,在VERICUT中设置机床参数和数控系统,添加刀具的轨迹G代码,进行加工过程的仿真,检验设计的阴极齿形状是否合理以及确定加工过程中出现的过切或欠切情况,优化阴极。研究表明,在电解加工阴极设计中采用VERICUT是一条可行的途径。