Application of a hybrid Taguchi grey approach for determining the optimal parameters on wire electrical discharge machining of Ti6Al4V

Khan, Muhammed Anaz; Thejasree, P.; Natarajan, Manikandan; Narasimhamu, K. L.

doi:10.1007/s12008-023-01440-3

Cited by 24 publications

(3 citation statements)

References 23 publications

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“…Through the use of ANOVA and the Taguchi-based grey method, this article elucidates the impact of a number of factors that have a momentous impact on the output of the WEDM process [11]. AI tools were developed for prediction and the performance of the models they use was analyzed in order to visualize the necessary performance factor.…”

Section: Resultsmentioning

confidence: 99%

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Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Natarajan¹,

Thejasree²,

Giri

et al. 2023

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Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.

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Section: Resultsmentioning

confidence: 99%

“…The ability of the hybrid learning was also inspected by subsequent Equations ( 10) and (11). The data attained from the assessment are depicted in Table 3.…”

Section: Efficiency Of Developed Predictive Modelsmentioning

confidence: 99%

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Natarajan¹,

Thejasree²,

Giri

et al. 2023

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“…Applications in the real world are restricted due to the high thermal stress [6]. Electron beam welding [7,8] stops electrode material from becoming contaminated, but it can only be done in a vacuum and requires two beam passes for welding Inconel, one of which must be fully penetrative [9]. It has been determined that FSW cannot be used for Inconel weldments [10,11], hence edge joint arrangement is not an option.…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid ANFIS Tool for Laser Beam Welding of Inconel 625 Alloy

Palanisamy,

Devaraju,

Natarajan

et al. 2023

DFMA

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Over the past decade, there has been a steady rise in the application of laser sources; this has led to reduced costs and enhanced output. One aspect that has played a part and helped to the quickening of this phenomena is the invention of new techniques such as laser beam welding systems. These developments in technology have made it feasible to produce both tiny and large parts. The usage of nickel alloy can provide considerable benefits in a wide variety of contexts. The advantages acquired from combining difficult materials, such as metal alloys, are responsible for these benefits. Manufacturing equipment for the chemical, nuclear, maritime, aeronautical, and automotive industries all rely on components made from Inconel 625. It can withstand potentially corrosive situations without losing its outstanding mechanical qualities. Investigation of nickel alloy weldments is required for the improvement of the production method. The goal of this piece is to develop an ANFIS (Adaptive Neuro Fuzzy Inference System) based on Grey theory that can reliably forecast LBW variables. The values predicted by the model were compared with the results of the experiments, and it was discovered that the values found in the experiments are closely related to the values anticipated by the model. The manufacturer can make decisions that are more in accordance with the available facts due to the evidence provided by the performance investigation.

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Application of Hybrid Artificial Intelligence Approach for Forecast of Performance Metrics in Sustainable Wire Electro Discharge Machining of SAE 1010 Low Carbon Steel

Thejasree,

Manikandan,

Satish

et al. 2024

Lecture Notes on Multidisciplinary Industrial Engineering

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Application of a hybrid Taguchi grey approach for determining the optimal parameters on wire electrical discharge machining of Ti6Al4V

Cited by 24 publications

References 23 publications

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Application of Hybrid ANFIS Tool for Laser Beam Welding of Inconel 625 Alloy

Application of Hybrid Artificial Intelligence Approach for Forecast of Performance Metrics in Sustainable Wire Electro Discharge Machining of SAE 1010 Low Carbon Steel

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