3,048 research outputs found

    Using multi-objective optimization to design parameters in electro-discharge machining by wire

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    The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD) of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to noise signal (S / N), which for this study is the variability of the hardness of samples from 6061, also studied the behaviour of the output parameters as the material removal rate (MRR) and surface roughness (Ra), subsequently took the RPD orthogonal array and characterized the individuals in the population, each optimal value is a gene and each possible solution is a chromosome, used multi-objective optimization using Non-dominated Sorting Genetic Algorithm to cross and mutate this population to generate better results MRR and Ra

    Investigations on Machining Aspects of Inconel 718 During Wire Electro-Discharge Machining (WEDM): Experimental and Numerical Analysis

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    Wire electro- discharge machining (WEDM) is known as unique cutting in manufacturing industries, especially in the good tolerance with intricate shape geometry in die industry. In this study the workpiece has been chosen as Inconel 718. Inconel 718 super alloy is widely used in aerospace industries. This nickel based super alloy has excellent resistance to high temperature, mechanical and chemical degradations with toughness and work hardening characteristics materials. Due to these properties, the machinability studies of this material have been carried-out in this study. The machining of Inconel 718 using variation of wire electrode material (brass wire electrode and zinc coated brass wire) with diameter equal to 0.20mm has been carried out. The objective of this study is mainly to investigate the various WEDM process parameters and performance of wire electrodes materials on Inconel 718 with various types of cutting. The optimal process parameter setting for each of wire electrode material has been obtained for multi-objective response. The kerf width, Material Removal Rate (MRR) and surface finish, corner error, corner deviation and angular error are the responses which are function of process variables viz. pulse-on time, discharge current, wire speed, flushing pressure and taper angle. The non-linear regression analysis has been developed for relationship between the process parameter and process characteristics. The optimal parameters setting have been carried out using multi-objective nature-inspired meta-heuristic optimization algorithm such as Whale Optimization Algorithm (WOA) and Gray Wolf Optimizer (GWO). Lastly numerical model analysis has been carried out to determine MRR and residual stress using ANSYS software and MRR model validated with the experimental results. The overlapping approach has been adopted for solving the multi-spark problem and validate with the experimental results

    Parametric optimization MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi based utility approach

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    Abstract: This paper reports the effect of process parameters on material removal rate (MRR) and surface roughness (Ra) in wire electro discharge machining of AISI D2 steel. The experiments were performed by different cutting conditions of pulse on time (Ton), pulse off time (Toff), servo voltage (SV) and wire feed (WF) by keeping workpiece thickness constant. Taguchi L27 orthogonal array of experimental design is employed to conduct the experiments. Multi-objective optimization was performed using Taguchi based utility approach to optimize MRR and Ra. Analysis of means and variance on to signal to noise ratio was performed for determining the optimal parameters. It reveals that the combination of Ton3, Toff1, SV1, WF2 parameter levels is beneficial for maximizing the MRR and minimizing the Ra simultaneously. The results indicated that the pulse on time is the most significant parameter affects the MRR and Ra. The melted droplets, solidified debris around the craters, cracks, and blow holes were observed on the machined surface for a higher pulse on time and lower servo voltage. Recast layer thickness increased from an increase in pulse on time duration. The machined surface hardness of D2 steel is increased due to the repetitive quenching effect and formation oxides on the machined surface

    A case study on Application of FUZZY logic in Electrical Discharge Machining(EDM)

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    Electrical Discharge Machining (EDM) is one of the most accurate manufacturing processes available for creating complex or simple shapes and geometries within parts and assemblies. EDM works by eroding material in the path of electrical discharges that form an arc between an electrode tool and the work piece. EDM manufacturing is quite affordable and a very desirable manufacturing process when low counts or high accuracy is required. Turn around time can be fast and depends on manufacturer back log. The EDM system consists of a shaped tool or wire electrode, and the part. The part is connected to a power supply. Sometimes to create a potential difference between the work piece and tool, the work piece is immersed in a dielectric (electrically non-conducting) fluid which is circulated to flush away debris

    Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

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    Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG

    Response surface methodology and artificial neural network-based models for predicting performance of wire electrical discharge machining of inconel 718 alloy

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    This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives

    Machining of Stainless Steels and Alloys Using Non-Traditional Machining Processes

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    Stainless steels and alloys are characterized primarily by their corrosion resistance, high strength, ductility, etc. used for various advanced applications like automotive and aerospace, sugar refineries, construction materials, etc. Many advanced high-speed machineries /systems need fine quality of parts to provide good performance in its working conditions. The machining of stainless steel and its alloys is of interest, because, of its excellent mechanical properties. Stainless steels and alloys are machined generally by traditional machining processes. But complex shapes and features on products are difficult task with the use of traditional metal cutting techniques. To machine the advanced materials to produce high dimensional accuracy and generation of intricate shapes in difficult-to-machine materials like stainless steels and alloys, nontraditional machining (NTM) techniques are now attractive the viable choices. To attain improved machining performance of the NTM processes, it is always necessary to find the optimal combinations of various process input parameters of those processes. In the present chapter, some aspects of machining of stainless steel and alloys using NTM processes such as electric discharge machining (EDM) and wire EDM, are discussed and some concluding remarks have been drawn from the study

    Multi objective machining estimation model using orthogonal and neural network

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    Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems

    Experimental Studies on Machinability of Inconel Super Alloy during Electro-Discharge Machining: Emphasis on Surface Integrity and Metallurgical Characteristics of the EDMed Work Surface

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    Inconel alloys are Nickel-Chromium based high temperature super alloys widely applied in aerospace, marine, nuclear power generation; chemical, petrochemical and process industries. Execution of traditional machining operations on Inconel super alloy is quite difficult due to its very low thermal conductivity which increases thermal effects during machining operations. Inconel often exhibits strong work hardening behavior, high adhesion characteristics onto the tool face, and thereby alters cutting process parameters to a remarkable extent. Additionally, Inconel may contain hard abrasive particles and carbides that create excessive tool wear; and, hence, surface integrity of the end product appears disappointing. The extent of tool life is substantially reduced. Thus, Inconel super alloys are included in the category of ‘difficult-to-cut’ materials. In view of the difficulties faced during conventional machining, non-traditional machining routes like Electro-Discharge Machining (EDM), Wire Electro-Discharge Machining (WEDM), micro-machining (micro-electro-discharge drilling) etc. are being attempted for processing of Inconel in order to achieve desired contour and intricate geometry of the end product with reasonably good dimensional accuracy. However, low material removal rate and inferior surface integrity seem to be a challenge. In this context, the present dissertation has aimed at investigating machining and machinability aspects of Inconel super alloys (different grades) during electro-discharge machining. Effects of process control parameters (viz. peak discharge current, pulse-on time, gap voltage, duty factor, and flushing pressure) on influencing EDM performance in terms of Material Removal Rate (MRR), Electrode Wear Rate (EWR) and Surface Roughness (SR) of the EDMed Inconel specimens have been examined. Morphology along with topographical features of the EDMed Inconel work surface have been studied in view of severity of surface cracking and extent of white layer depth. Additionally, X-Ray Diffraction (XRD) analysis has been carried out to study metallurgical characteristics of the EDMed work surface of Inconel specimens (viz. phases present and precipitates, extent of grain refinement, crystallite size, and dislocation density etc.) in comparison with that of ‘as received’ parent material. Results, obtained thereof, have been interpreted with relevance to Energy Dispersive X-ray Spectroscopy (EDS) analysis, residual stress and micro-indentation hardness test data. Effort has been made to determine the most appropriate EDM parameters setting to optimize MRR, EWR, along with Ra (roughness average), relative Surface Crack Density (SCD), as well as relative White Layer Thickness (WLT) observed onto the EDMed work surface of Inconel specimens. Moreover, an attempt has been made to examine the ease of electro-discharge machining on Inconel work materials using Deep Cryogenically Treated (DCT) tool/workpiece. A unified attempt has also made to compare surface integrity and metallurgical characteristics of the EDMed Inconel work surface as compared to the EDMed A2 tool steel (SAE 304SS) as well as EDMed Titanium alloy (Ti-6Al-4V)
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