FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280493725> ?p ?o ?g. }

• W4280493725 endingPage "100179" @default.
• W4280493725 startingPage "100179" @default.
• W4280493725 abstract "In the era of advancement and progressive fourth industrial revolution culture, the demand of advanced and smart engineering materials has been increased. In this way, shape memory alloys are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft component components, military instruments, fabricating elements, and bio-medical devices, among others. This paper has been aimed to attempt the machine learning (ML) algorithms-based optimization of the different process inputs in electrical discharge machining of Cu-based shape memory alloy. The current study focused on study the behavior of response parameters along with the variation in machining input parameters The considered process input factors are namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV) and their effects were studied on dimensional deviation (DD) and tool wear rate (TWR). The central composite design matrix has been employed for planning the main runs. The 2-D and 3-D graphs represents the behavior of the response parameters along with variations in the machining inputs. The novelty of the work is machining of Cu-based Shape Memory Alloy (SMA) in EDM operations and optimization of parameters using Machine Learning techniques. Furthermore, machine learning based, single and multi-objective optimization of investigated responses were conducted using the desirability approach, Genetic Algorithm (GA) and Teacher Learning based Optimization (TLBO) techniques. The parametric combination attained for optimization of multiple responses (TWR and DD) is: Ton ​= ​90.10 ​μs, Toff ​= ​149.69 ​μs, Ip ​= ​24.59 A & GV ​= ​60 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V, using desirability approach, GA method and TLBO method, respectively." @default.
• W4280493725 created "2022-05-22" @default.
• W4280493725 creator A5010789264 @default.
• W4280493725 creator A5062046298 @default.
• W4280493725 creator A5068488502 @default.
• W4280493725 date "2022-01-01" @default.
• W4280493725 modified "2023-10-18" @default.
• W4280493725 title "Machine learning algorithms based advanced optimization of EDM parameters: An experimental investigation into shape memory alloys" @default.
• W4280493725 cites W1935861078 @default.
• W4280493725 cites W1969179886 @default.
• W4280493725 cites W1973103269 @default.
• W4280493725 cites W1973565005 @default.
• W4280493725 cites W1987828839 @default.
• W4280493725 cites W1987940150 @default.
• W4280493725 cites W2010052821 @default.
• W4280493725 cites W2012673913 @default.
• W4280493725 cites W2095609071 @default.
• W4280493725 cites W2137872020 @default.
• W4280493725 cites W2139503473 @default.
• W4280493725 cites W2149313997 @default.
• W4280493725 cites W2305535624 @default.
• W4280493725 cites W2344141298 @default.
• W4280493725 cites W2402057148 @default.
• W4280493725 cites W2587744121 @default.
• W4280493725 cites W2791648710 @default.
• W4280493725 cites W2794936066 @default.
• W4280493725 cites W2801419952 @default.
• W4280493725 cites W2806644421 @default.
• W4280493725 cites W2900582216 @default.
• W4280493725 cites W2916034078 @default.
• W4280493725 cites W2919376911 @default.
• W4280493725 cites W2931508243 @default.
• W4280493725 cites W2935716154 @default.
• W4280493725 cites W2938077693 @default.
• W4280493725 cites W2965440425 @default.
• W4280493725 cites W2967167542 @default.
• W4280493725 cites W2969862419 @default.
• W4280493725 cites W3024007160 @default.
• W4280493725 cites W3034354398 @default.
• W4280493725 cites W3088005740 @default.
• W4280493725 cites W3097396444 @default.
• W4280493725 cites W3138613784 @default.
• W4280493725 cites W3150897037 @default.
• W4280493725 cites W3162726234 @default.
• W4280493725 cites W3168582173 @default.
• W4280493725 cites W3175432212 @default.
• W4280493725 cites W3176457459 @default.
• W4280493725 cites W3188017575 @default.
• W4280493725 cites W3195224051 @default.
• W4280493725 cites W3200831409 @default.
• W4280493725 cites W3202123766 @default.
• W4280493725 cites W3208751895 @default.
• W4280493725 cites W3214069043 @default.
• W4280493725 cites W3214664155 @default.
• W4280493725 cites W3215811824 @default.
• W4280493725 cites W4220655224 @default.
• W4280493725 doi "https://doi.org/10.1016/j.sintl.2022.100179" @default.
• W4280493725 hasPublicationYear "2022" @default.
• W4280493725 type Work @default.
• W4280493725 citedByCount "3" @default.
• W4280493725 countsByYear W42804937252023 @default.
• W4280493725 crossrefType "journal-article" @default.
• W4280493725 hasAuthorship W4280493725A5010789264 @default.
• W4280493725 hasAuthorship W4280493725A5062046298 @default.
• W4280493725 hasAuthorship W4280493725A5068488502 @default.
• W4280493725 hasBestOaLocation W42804937251 @default.
• W4280493725 hasConcept C105795698 @default.
• W4280493725 hasConcept C111919701 @default.
• W4280493725 hasConcept C11413529 @default.
• W4280493725 hasConcept C117251300 @default.
• W4280493725 hasConcept C119599485 @default.
• W4280493725 hasConcept C119857082 @default.
• W4280493725 hasConcept C127413603 @default.
• W4280493725 hasConcept C140075996 @default.
• W4280493725 hasConcept C154945302 @default.
• W4280493725 hasConcept C165801399 @default.
• W4280493725 hasConcept C33923547 @default.
• W4280493725 hasConcept C41008148 @default.
• W4280493725 hasConcept C49097943 @default.
• W4280493725 hasConcept C523214423 @default.
• W4280493725 hasConcept C78519656 @default.
• W4280493725 hasConcept C83469408 @default.
• W4280493725 hasConcept C8880873 @default.
• W4280493725 hasConcept C98045186 @default.
• W4280493725 hasConceptScore W4280493725C105795698 @default.
• W4280493725 hasConceptScore W4280493725C111919701 @default.
• W4280493725 hasConceptScore W4280493725C11413529 @default.
• W4280493725 hasConceptScore W4280493725C117251300 @default.
• W4280493725 hasConceptScore W4280493725C119599485 @default.
• W4280493725 hasConceptScore W4280493725C119857082 @default.
• W4280493725 hasConceptScore W4280493725C127413603 @default.
• W4280493725 hasConceptScore W4280493725C140075996 @default.
• W4280493725 hasConceptScore W4280493725C154945302 @default.
• W4280493725 hasConceptScore W4280493725C165801399 @default.
• W4280493725 hasConceptScore W4280493725C33923547 @default.
• W4280493725 hasConceptScore W4280493725C41008148 @default.
• W4280493725 hasConceptScore W4280493725C49097943 @default.
• W4280493725 hasConceptScore W4280493725C523214423 @default.

• next