A Comprehensive Analysis of Surface Roughness, Vibration, and Acoustic Emissions Based on Machine Learning during Hard Turning of AISI 4140 Steel

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dc.authoridBinali, Rustem/0000-0003-0775-3817
dc.authoridAKKUS, Harun/0000-0002-9033-309X
dc.authoridKUNTOGLU, MUSTAFA/0000-0002-7291-9468
dc.contributor.authorAsilturk, Ilhan
dc.contributor.authorKuntoglu, Mustafa
dc.contributor.authorBinali, Rustem
dc.contributor.authorAkkus, Harun
dc.contributor.authorSalur, Emin
dc.date.accessioned2024-11-07T13:32:48Z
dc.date.available2024-11-07T13:32:48Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIndustrial materials are materials used in the manufacture of products such as durable machines and equipment. For this reason, industrial materials have importance in many aspects of human life, including social, environmental, and technological elements, and require further attention during the production process. Optimization and modeling play an important role in achieving better results in machining operations, according to common knowledge. As a widely preferred material in the automotive sector, hardened AISI 4140 is a significant base material for shaft, gear, and bearing parts, thanks to its remarkable features such as hardness and toughness. However, such properties adversely affect the machining performance of this material system, due to vibrations inducing quick tool wear and poor surface quality during cutting operations. The main focus of this study is to determine the effect of parameter levels (three levels of cutting speed, feed, and cutting depth) on vibrations, surface roughness, and acoustic emissions during dry turning operation. A fuzzy inference system-based machine learning approach was utilized to predict the responses. According to the obtained findings, fuzzy logic predicts surface roughness (88%), vibration (86%), and acoustic emission (87%) values with high accuracy. The outcome of this study is expected to make a contribution to the literature showing the impact of turning conditions on the machining characteristics of industrially important materials.
dc.identifier.doi10.3390/met13020437
dc.identifier.issn2075-4701
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85149197348
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/met13020437
dc.identifier.urihttps://hdl.handle.net/11480/15621
dc.identifier.volume13
dc.identifier.wosWOS:000940021800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMetals
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectturning
dc.subjectAISI 4140
dc.subjectsurface roughness
dc.subjectvibration
dc.subjectacoustic emissions
dc.subjectmachine learning
dc.titleA Comprehensive Analysis of Surface Roughness, Vibration, and Acoustic Emissions Based on Machine Learning during Hard Turning of AISI 4140 Steel
dc.typeArticle

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