Scopus
YÖKSİS ISSN Eşleşti
SJR Q1
Experimental and machine learning comparison for measurement the machinability of nickel based alloy in pursuit of sustainability
Measurement Journal of the International Measurement Confederation · Ağustos 2024
YÖKSİS Kayıtları — ISSN Eşleşmesi
Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods
2016 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering
2016 ISSN: 02632241 SCI-Expanded
Prof. Dr. HASAN ERDİNÇ KOÇER →
Point cloud filtering on UAV based point cloud
2019 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA ZEYBEK →
Investigation of progressive tool wear for determining of optimized machining parameters in turning
2019 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
Analysis of effect factors on thermoelectric generator using Taguchi method
2020 ISSN: 0263-2241 SCI
Dr. Öğr. Üyesi HAKAN TERZİOĞLU →
Decomposition of process damping ratios and verification of process damping model for chatter vibration
2012 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis
2012 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Optimization of tool geometry parameters for turning operations based on the response surface methodology
2011 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
A new process damping model for chatter vibration
2011 ISSN: 02632241 SCI-Expanded
Prof. Dr. SÜLEYMAN NEŞELİ →
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
2021 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
The determination of age and gender by implementing new image processing methods and measurements to dental X-ray images
2020 ISSN: 0263-2241 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey
2021 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MUSTAFA ZEYBEK →
Measuring curvature of trajectory traced by coupler of an optimal four-link spherical mechanism
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi OSMAN ACAR →
A CNN-SVM Study Based on Selected Deep Features for Grapevine Leaves Classification
2022 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
An S-band zero-IF SFCW through-the-wall radar for range, respiration rate, and DOA estimation
2021 ISSN: 0263-2241 SCI-Expanded Q1
Prof. Dr. İSMAİL SARITAŞ →
An experimental study: Detecting the respiration rates of multiple stationary human targets by stepped frequency continuous wave radar
2021 ISSN: 0263-2241 SCI-Expanded Q1
Prof. Dr. İSMAİL SARITAŞ →
An experimental study: Detecting the respiration rates of multiple stationary human targets by stepped frequency continuous wave radar
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
An S-band zero-IF SFCW through-the-wall radar for range, respiration rate, and DOA estimation
2021 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi YUNUS EMRE ACAR →
A CNN-SVM study based on selected deep features for grapevine leaves classification
2022 ISSN: 0263-2241 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →
Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends
2022 ISSN: 0263-2241 SCI-Expanded Q1
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
Makale Bilgileri
ISSN02632241
Yayın TarihiAğustos 2024
Cilt / Sayfa236
Scopus ID2-s2.0-85196309103
Özet
Inconel 718 super alloy, which is widely used in the aerospace industry, has a high fracture resistance, and withstand to high temperatures. The alloy contains mainly Nickel, Chromium and Molybdenum elements in its chemical composition put it among difficult to cut materials. In this context, this study aims to improve the machinability of Inconel 718 superalloy by examining the effect of dry and MQL machining environments while measuring machinability indicators during milling. Tribological aspects considered since the wear, friction and lubrication behavior have a dramatic impact on responses such as tool wear, surface integrity and chip morphology. Microstructural and graphical results were assessed in terms of varying levels of cutting parameters and lubrication conditions. Comparison analysis between MQL and dry media indicated that MQL produces better surface topography and chip morphology, longer tool life in addition to improvement on surface roughness (up to 23.7 %) and cutting temperatures (up to 27.4 %). The root mean square error (RMSE) and coefficient of determination (R2) metrics were utilized to evaluate the findings in the course of machine learning. According to the mean and 95 % confidence interval of RMSE, error rates were found to be good and R2 varied between 67 % and 98 %. Predicted results are in a good agreement with the experimental data which indicated the applicability of machine learning algorithms on sustainable methods of machining.
Yazarlar (1)
1
Rüstem Binali
ORCID: 0000-0003-0775-3817
Anahtar Kelimeler
Inconel 718
Machinability
Machine learning
Milling
Superalloy
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Measurement: Journal of the International Measurement Confederation
Q1
SJR Skoru1,244
H-Index146
YayıncıElsevier B.V.
ÜlkeNetherlands
Applied Mathematics (Q1)
Condensed Matter Physics (Q1)
Education (Q1)
Electrical and Electronic Engineering (Q1)
Instrumentation (Q1)
Statistics and Probability (Q1)
Metrikler
28
Atıf
1
Yazar
5
Anahtar Kelime