Parametric Optimization and Influence of Near‐Dry WEDM Variables on Nitinol Shape Memory Alloy

Parametric Optimization and Influence of Near‐Dry WEDM Variables on Nitinol Shape Memory Alloy

Nitinol‐shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for t...

Full description

Bibliographic Details
Main Authors: Chaudhari, R. (Author), Giasin, K. (Author), Kevalramani, A. (Author), Khanna, S. (Author), Patel, V.K (Author), Pimenov, D.Y (Author), Vora, J. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Analysis of variance (ANOVA)
'Dry' [
Electric discharge machining
Electric discharges
Environmental impact
Learning algorithms
Material removal rate
Morphology
Near‐dry wire electric discharge machining
near‐dry wire electric discharge machining (WEDM)
nitinol
Nitinol
Nitinol shape memory alloy
Optimisations
optimization
Optimization algorithms
Parameter estimation
shape memory alloys
Surface defects
Surface morphology
Surface roughness
teaching–learning based optimization (TLBO) algorithm
Teaching–learning based optimization algorithm
Teaching-learning-based optimizations
Wire
Wire electric discharge machining
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items