Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm Article Swipe
N. Manikandan
,
Thejasree Pasupuleti
,
Jayant Giri
,
Neeraj Sunheriya
,
Lakshmi Narasimhamu Katta
,
Rajkumar Chadge
,
Chetan Mahatme
,
Pallavi Giri
,
Saurav Mallik
,
Kanad Ray
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/info14080439
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/info14080439
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.
Related Topics
Concepts
Electrical discharge machining
Machinability
Taguchi methods
Machining
Automotive industry
Computer science
Enhanced Data Rates for GSM Evolution
Materials science
Titanium alloy
Surface roughness
Mechanical engineering
Orthogonal array
Algorithm
Alloy
Machine learning
Engineering
Metallurgy
Composite material
Artificial intelligence
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info14080439
- https://www.mdpi.com/2078-2489/14/8/439/pdf?version=1691071475
- OA Status
- gold
- Cited By
- 75
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385544214
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385544214Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/info14080439Digital Object Identifier
- Title
-
Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-03Full publication date if available
- Authors
-
N. Manikandan, Thejasree Pasupuleti, Jayant Giri, Neeraj Sunheriya, Lakshmi Narasimhamu Katta, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, Saurav Mallik, Kanad RayList of authors in order
- Landing page
-
https://doi.org/10.3390/info14080439Publisher landing page
- PDF URL
-
https://www.mdpi.com/2078-2489/14/8/439/pdf?version=1691071475Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2078-2489/14/8/439/pdf?version=1691071475Direct OA link when available
- Concepts
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Electrical discharge machining, Machinability, Taguchi methods, Machining, Automotive industry, Computer science, Enhanced Data Rates for GSM Evolution, Materials science, Titanium alloy, Surface roughness, Mechanical engineering, Orthogonal array, Algorithm, Alloy, Machine learning, Engineering, Metallurgy, Composite material, Artificial intelligence, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
75Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 30, 2024: 30, 2023: 15Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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