Analytical Modelling of Energy Density and Optimization of the EDM Machining Parameters of Inconel 600

Salcedo, Alexia Torres; Puertas, I.; Pérez, C.J. Luis

doi:10.3390/met7050166

Cited by 36 publications

(33 citation statements)

References 31 publications

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“…In manufacturing processes, is it quite common for certain technological variables to have a more regular response behavior and, therefore, they can be modeled by methodologies such as RSM, and, on the other hand, there also exist some other technological variables that cannot be adequately modeled using conventional regression techniques. For example, in Reference [61] it is shown that the arithmetic average roughness (Ra) can be approximated by a polynomial regression using RSM, in an EDM process. However, due to the variability of the data, the wear that the electrode (EW) undergoes in this EDM process cannot be adequately modeled by using RSM, and for this reason, in order to adequately model this technological variable, it would be necessary to employ some other technique, for example based on using an ANFIS or an ANN.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, Torres at al. [61] employed a 4 3 factorial DOE for modeling the behavior of the arithmetical mean roughness (Ra), the electrode wear (EW) and the MRR in the EDM machining of an Inconel ® 600 alloy using Cu-C electrodes (Inconel®600 alloy is a registered trademark of the Special Metals Corporation group of Companies). In Lin et al [62] surface roughness in the end-milling process is analyzed as a function of spindle speed, cutting depth, and feed rate and machining vibration by using multiple regression and ANNs and in Karloopia et al [63] cutting speed, depth of cut, and feed are used as independent variables to obtain cutting force and tool wear in Al-Si-TiB2 composites by using linear regression.…”

Section: State Of the Artmentioning

confidence: 99%

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A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

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Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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“…The conductivity of the deionized water was 0.1 µS•cm −1 ; electric voltage V o = 270 V; and current intensity I e = 0.05 A. The frequency of the working impulses was f w = 0.2 MHz [49,50].…”

Section: Methodsmentioning

confidence: 99%

The Effect of TiC Additive on Mechanical and Electrical Properties of Al2O3 Ceramic

et al. 2018

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In this study the influence of TiC content on the mechanical and electrical properties of Al2O3-TiC composites containing 30 and 40 vol.% TiC were investigated. The Vickers hardness and fracture toughness of the composites increased with the addition of TiC phase. The composite with 40 vol.% TiC showed the highest flexural strength (687 ± 39 MPa), fracture toughness (7.8 ± 0.4 MPa·m1/2) and hardness (22.3 ± 0.3 GPa) with a homogeneous distribution of the second phase within the ceramic matrix. Besides enhanced mechanical properties, it was found that ceramic composites with more than 30 vol.% TiC fabricated by the spark plasma sintering possess sufficient electrical conductivity for electrical discharge machining as well. Therefore, they do not limit the flexibility of the shape, and any intricate parts can be easily made with these composites which can be recommended for the production of cutting inserts in the tools for machining of superhard hardened steels, hard-to-machine materials, composites and other materials used in mechanical engineering.

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“…They suggested mathematical models for the determination of the optimal combination of significant technological parameters in order to minimize microhardness and total HAZ depth variations of tool steel EN X32CrMoV12-28 after die-sinking EDM with a SF-Cu electrode. Optimization of EDM machining parameters of Inconel 600 was presented in [11]. They analytically modeled the energy density, which is being absorbed by the workpiece and the electrode, and experimentally confirmed that the negative polarity leads to a higher material removal rate, higher electrode wear and higher surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Electro-Discharge Texturing Parameters for Steel Sheets’ Finishing Rollers

2020

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Exterior car-body parts are made of steel or aluminum sheets. Their formability and appearance after painting depends not only on the mechanical properties but also on their surface texture. The surface roughness characteristics, the roughness average Ra and the peak count Pc per centimeter depend on the texture of rolling mill's finishing rollers, their wear and the degree of removal by the rolling mill. The research was carried out on heat-treated finishing rollers on the surface of which a controlled texture was created by changing the electro-discharge texturing (EDT) parameters. Parameters and the number of electro-discharge texturing experiments were optimized using full four-factor experiment techniques at the upper and lower levels of the parameters in the form of 2 4 . The significance of the impact of individual EDT parameters and their interactions was identified based on the variance results. The ANOVA variance analysis results confirmed that the roughness Ra and the peak count Pc depend primarily on peak current (I p ), discharge peak voltage (U p ), pulse on time (P ont ) and pulse off time (P offt ). Optimization of the effect of the above parameters on the target roughness Ra T,FR values and the peak count Pc T,FR of finishing rollers was performed by the response surface methodology (RSM). Obtained regression models describe relationships between the input parameters of the electro-discharge texturing of finishing rollers and the output characteristics of the Ra T,FR and the Pc T,FR texture to a very high degree. The reliability of the electro-discharge texturing process of working rollers was assessed using the process capability index C pk .

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Analytical Modelling of Energy Density and Optimization of the EDM Machining Parameters of Inconel 600

Cited by 36 publications

References 31 publications

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

The Effect of TiC Additive on Mechanical and Electrical Properties of Al2O3 Ceramic

Optimization of Electro-Discharge Texturing Parameters for Steel Sheets’ Finishing Rollers

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