ArticleMaterials (Basel, Switzerland)2021
Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Performance optimization of wire EDM of Nitinol shape memory alloy using BBD RSM and TLBO with alumina nano graphene and MWCNT Powder mixed dielectric.Scientific reports · 2026Article
- Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN).Micromachines · 2025Article
- Experimental Investigations of Using Aluminum Oxide (AlMicromachines · 2023Article
- Experimental Investigation into the Influence of the Process Parameters of Wire Electric Discharge Machining Using Nimonic-263 Superalloy.Materials (Basel, Switzerland) · 2023Article
- Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy.Nanomaterials (Basel, Switzerland) · 2022Article
- Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA.Materials (Basel, Switzerland) · 2022Article
- Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy.Micromachines · 2022Article
- Design of a Morphing Skin with Shape Memory Alloy Based on Equivalent Thermal Stress Approach.Micromachines · 2022Article
- Multi-Response Optimization of AlMaterials (Basel, Switzerland) · 2022Article
- Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM.Materials (Basel, Switzerland) · 2021Article
- Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM.Materials (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors.
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Abstract
In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi's L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm
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