ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$-EDM

Fabrication of micro-holes has been carried out in Inconel 718 using micro electrical discharge machining operation. Artificial neural network modelling has been carried out to predict Material Removal Rate, Overcut effect and Recast Layer thickness. The training, testing and validation data sets were collected by conducting experiments. It is observed that ANN is a powerful prediction tool. It provides agreeable results when experimental and predicted data are compared. Further optimization of the process variables has been carried out using different meta heuristic approaches like Elitist Teaching learning based optimization, Multi-Objective Differential Evolution and Multi-Objective Optimization using an Artificial Bee Colony algorithm. The comparisons are carried out to improve the accuracy of the model on the basis of Pareto front solutions.     

Identification of new material model for machining simulation of Inconel 718 alloy and the effect of tool edge geometry on microstructure changes

Johnson–Cook (J-C) material model is often used for Finite Element (FE) modeling of cutting processes and it affects significantly the results of simulation. Since experimentally determination of J-C equation parameters is an expensive and time-consuming task, in this paper, a new and efficient methodology was implemented based on the evolutionary optimization algorithm to identify new J-C material constants for Inconel 718 superalloy. Then, orthogonal cutting process of Inconel 718 was simulated using the new material models. The results of simulation were compared extensively with experimental results of cutting forces, chip geometry, and maximum temperature to select and then validate the most suitable material model. After that, a user subroutine was implemented in FE code to model microstructure changes using the dynamic recrystallization mechanism in cutting process of Inconel 718. The Zener–Hollomon and Hall–Petch equations were used to respectively predict the grain size and hardness. The results of simulation were verified successfully with corresponding experiments in terms of near-surface profile. At the end, the effect of tool edge geometry including honed and chamfered tool edge was investigated on microstructure changes.     

A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

The present work attempts to study the effect of important machining variables on performance characteristics such as material removal rate and tool wear in turning of Inconel 718 using chemical vapour deposition (CVD) coated tungsten carbide (WC) tool. A three dimensional machining model using Lagrangian approach has been developed using DEFORM 3D. The machining simulation is carried out to predict the flank wear and material removal rate (MRR). Flank wear is calculated using Usui’s wear model in the simulation model. The results from simulation model are compared with experimental data generated by the use of Taguchi’s L₁₆ orthogonal array for reducing the experimental runs. Analysis of variance (ANOVA) is performed to identify the most influencing variables for both the performance characteristics. It is found that simulation results are in good agreement with experimental results. A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.     

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR \((\hbox {R}_{\mathrm{a}})\) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.     

Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.

     

Sensitivity analysis of material input data influence on machining induced residual stress prediction in Inconel 718

Inconel 718 is commonly used in structural critical components of aircraft engines due to its properties at high temperatures. In order manufacture the final part, these components have to be machined, so the final surface integrity obtained after machining becomes a key issue. Residual stresses, which are included in surface integrity, are an important issue. Although much of the research carried out on machining induced residual stresses has been empirical, finite element modelling appears to be a complementary solution to gain understanding of it. However, some of the major drawbacks still need to be solved before it can become a reliable tool for industry, such us the identification of input parameters and computational cost. This paper deals with the study of machining induced residual stresses. An orthogonal cutting 2D finite element model was used and a sensitivity analysis was conducted to determine the influence of model input data on the predicted residual stresses. The results obtained from the sensitivity analysis showed that material constitutive law was the most relevant input data when predicting residual stress fields. Importantly the material behaviour at a high heating rate in adition to high strain rate must be considered.     

Potential ANN prediction model for multiperformances WEDM on Inconel 718

Neural Computing and Applications - This paper proposes a machining performance prediction approach on multiple performances of wire electrical discharge machining (WEDM) on Inconel 718. Artificial...     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks

This study covers two main subjects: (i) The experimental and theoretical analysis: the cutting forces and indirectly cutting tool stresses, affecting the cutting tool life during machining in metal cutting, are one of very important parameters to be necessarily known to select the economical cutting conditions and to mount the workpiece on machine tools securely. In this paper, the cutting tool stresses (normal, shear and von Mises) in machining of nickel-based super alloy Inconel 718 have been investigated in respect of the variations in the cutting parameters (cutting speed, feed rate and depth of cut). The cutting forces were measured by a series of experimental measurements and the stress distributions on the cutting tool were analysed by means of the finite element method (FEM) using ANSYS software. ANSYS stress results showed that in point of the cutting tool wear, especially from von Mises stress distributions, the ceramic cutting insert may be possible worn at the distance equal to the depth of cut on the base cutting edge of the cutting tool. Thence, this wear mode will be almost such as the notch wear, and the flank wear on the base cutting edge and grooves in relief face. In terms of the cost of the process of machining, the cutting speed and the feed rate values must be chosen between 225 and 400 m/min, and 0.1 and 0.125 mm/rev, respectively. (ii) The mathematical modelling analysis: the use of artificial neural network (ANN) has been proposed to determine the cutting tool stresses in machining of Inconel 718 as analytic formulas based on working parameters. The best fitting set was obtained with ten neurons in the hidden-layer using back propagation algorithm. After training, it was found the R² values are closely 1.     

Optimization of magnetic field assisted EDM using the continuous ACO algorithm

In this paper, a rotary tool with rotary magnetic field has been used to better flushing of the debris from the machining zone in electrical discharge machining (EDM) process. Two adaptive neuro-fuzzy inference system (ANFIS) models have been designed to correlate the EDM parameters to material removal rate (MRR) and surface roughness (SR) using the data generated based on experimental observations. Then continuous ant colony optimization (CACO) technique has been used to select the best process parameters for maximum MRR and specified SR. Here, the process parameters are magnetic field intensity, rotational speed and product of current and pulse on-time. Also, ANFIS models of MRR and SR are the objective and constraint functions for CACO, respectively. Experimental trials divided into three main regimes of low energy, the middle energy and the high energy. Results showed that the CACO technique which used the ANFIS models as objective and constrain functions can successfully optimize the input conditions of the magnetic field assisted rotary EDM process.     

Optimization and predictive modeling using S/N,RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18 Å, dielectric liquid pressure of 58.78 bar and electrode tool rotational speed of 100 rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

     

Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm

Selection of the optimal values of different operating parameters is of utmost importance for enhancing the performance of various non-traditional machining (NTM) processes. The performance measures (responses) of different NTM processes usually include metal removal rate, surface roughness, radial overcut, tool wear rate, heat affected zone, etc. In this paper, artificial bee colony (ABC) algorithm is employed to search out the optimal combinations of different operating parameters for three widely used NTM processes, i.e. electrochemical machining, electrochemical discharge machining and electrochemical micromachining processes. Both the single and multi-objective optimization problems for the considered NTM processes are solved using this algorithm. The results obtained while applying the ABC algorithm for parametric optimization of these three NTM processes are compared with those derived by the past researchers, which prove the applicability and suitability of the ABC algorithm in enhancing the performance measures of the considered NTM processes.     

Glowworm swarm optimization (GSO) for optimization of machining parameters

This study proposes glowworm swarm optimization (GSO) algorithm to estimate an improved value of machining performance measurement. GSO is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. To the best our knowledge, GSO algorithm has not yet been used for optimization practice particularly in machining process. Three cutting parameters of end milling that influence the machining performance measurement, minimum surface roughness, are cutting speed, feed rate and depth of cut. Taguchi method is performed for experimental design. The analysis of variance is applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. GSO has improved machining process by estimating a much lower value of minimum surface roughness compared to the results of experimental and particle swarm optimization.     

Modeling,analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches,based on finite element results

Recently, twist extrusion has found extensive applications as a novel method of severe plastic deformation for grain refining of materials. In this paper, two prominent predictive models, response surface method and artificial neural network (ANN) are employed together with results of finite element simulation to model twist extrusion process. Twist angle, friction factor and ram speed are selected as input variables and imposed effective plastic strain, strain homogeneity and maximum punch force are considered as output parameters. Comparison between results shows that ANN outperforms response surface method in modeling twist extrusion process. In addition, statistical analysis of response surface shows that twist extrusion and friction factor have the most and ram speed has the least effect on output parameters at room temperature. Also, optimization of twist extrusion process was carried out by a combination of neural network model and multi-objective meta-heuristic optimization algorithms. For this reason, three prominent multi-objective algorithms, non-dominated sorting genetic algorithm, strength pareto evolutionary algorithm and multi-objective particle swarm optimization (MOPSO) were utilized. Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points.     

A new force-depth model for robotic abrasive belt grinding and confirmation by grinding of the Inconel 718 alloy

Robotic abrasive belt grinding has been successfully applied to the grinding and polishing of aerospace parts. However, due to the flexible characteristics of robotic abrasive belt grinding and the time-varying characteristics of the polishing contact force, as well as the plastic and difficult-to-machine material properties of Inconel 718 alloy, it is very difficult to control the actual removal depth and force of the polished surface, which brings great challenges to robot automatic polishing. Therefore, the relationship between the grinding force and the grinding depth in the robotic abrasive belt grinding is analyzed in detail, the robot machining pose error model considering the deformation of the grinding head is established, and the Inconel 718 alloy machining experiment of the robotic abrasive belt grinding is designed. The mapping relationship between the grinding force and the grinding depth is obtained, and the grinding force ratio in the downgrinding and upgrinding mode is discussed. The experimental and theoretical comparisons results show that with the increase of the grinding depress depth, both the grinding depth and the grinding force show an irregular increasing trend, and the increasing trend of the grinding force (increases by about 344.44%–445.45%) is obviously greater than that of the grinding depth (increases by about 52.94%). When the grinding depress depth is large (greater than 3 mm), the feed direction force and the normal force appear obvious secondary pressure peaks at the beginning and end of grinding, which has not been seen in previous studies. In addition, regardless of whether it is downgrinding or upgrinding, the grinding force ratio decreases with the increase of the depress depth, and the grinding force ratio of downgrinding (average 0.668) is smaller than that of upgrinding (average 0.724). This study provides a reference for robotic abrasive belt grinding, and the surface quality of Inconel 718 alloy of robotic abrasive belt grinding can be further improved through the optimization of force and depth.     

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (T _on), pulse-off time (T _off) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

     

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Over the past decade, the particle swarm optimization (PSO) has been an effective algorithm for solving single and multi-object optimization problems. Recently, the chemical reaction optimization (CRO) algorithm is emerging as a new algorithm used to efficiently solve single-object optimization.In this paper, we present HP-CRO (hybrid of PSO and CRO) a new hybrid algorithm for multi-object optimization. This algorithm has features of CRO and PSO, HP-CRO creates new molecules (particles) not only used by CRO operations as found in CRO algorithm but also by mechanisms of PSO. The balancing of CRO and PSO operators shows that the method can be used to avoid premature convergence and explore more in the search space.This paper proposes a model with modified CRO operators and also adding new saving molecules into the external population to increase the diversity. The experimental results of the HP-CRO algorithm compared to some meta-heuristics algorithms such as FMOPSO, MOPSO, NSGAII and SPEA2 show that there is improved efficiency of the HP-CRO algorithm for solving multi-object optimization problems.     

多目标微粒群优化算法及其应用研究进展*

多目标微粒群优化(MOPSO)算法是一类基于群体智能的新型全局多目标优化方法,已受到广泛关注,并在许多领域得到应用。针对近几年来MOPSO算法及其应用的进展进行了综述和评论。首先描述了MOPSO算法的基本框架;接着对MOPSO算法进行了分类和分析,并给出了MOPSO算法的一些改进策略;然后介绍了MOPSO算法的应用进展;最后,展望了MOPSO算法值得进一步研究的方向。     

Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes

Condition monitoring of the machining process is very important in today's precision manufacturing, especially in the electrical discharge machining (EDM). This paper introduces a fuzzy-based algorithm for prediction of material removal rate (MRR), tool wear ratio (TWR), and surface roughness (R_z, R_k) in the EDM and ultrasonic-assisted EDM (US/EDM) processes. In this system, discharge current, pulse duration, and ultrasonic vibration of tool are the input variables and outputs are MRR, TWR, R_z, and R_k. The proposed fuzzy model in this study provides a more precise and easy selection of EDM and US/EDM input parameters, respectively for the required MRR, TWR, R_z, and R_k, which leads to better machining conditions and decreases the machining costs. The fuzzy modeling of EDM and US/EDM were able to predict the experimental results with accuracies more than 90%.     

An efficient inverse approach for material hardening parameter identification from a three-point bending test

The cyclic three-point bending test has been frequently used for the determination of material hardening parameters. The advantage of this test is that it is simple to perform, and standard test equipment can be used. The disadvantage is that the material parameter identification requires some kind of inverse approach. The current authors have previously, successfully been utilizing a method, in which computed force–displacement relations have been fitted to corresponding experimental results. The test has been simulated by means of the Finite Element code LS-DYNA, and the material parameters have been determined by finding a best fit to the experimental results by means of the optimization tool LS-OPT, based on a response surface methodology. A problem is, however, that such simulations can be quite time consuming, since the Finite Element model has to be analyzed numerous times. In the current paper, an alternative numerical methodology will be described, in which instead calculated moment–curvature relations are fitted to experimental ones. This optimization procedure does not involve any solution of the FE problem. The Finite Element problem needs only to be solved a limited number of times in an outer iteration loop. This fact results in a considerable reduced computational cost. It is also demonstrated that the parameters determined by this new method correspond excellently to the ones determined by the conventional method.