Scopus
YÖKSİS Eşleşti
Understanding the effects of machinability properties of Incoloy 800 superalloy under different machining conditions using artificial intelligence methods
Materials Today Communications · Mart 2024
YÖKSİS Kayıtları
Understanding the effects of machinability properties of Incoloy 800 superalloy under different processing conditions using artificial intelligence methods
Materials Today Communications · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ ÜSAME ALİ USCA →
Makale Bilgileri
DergiMaterials Today Communications
Yayın TarihiMart 2024
Cilt / Sayfa38
Scopus ID2-s2.0-85186638145
Özet
Incoloy 800 is frequently used in high-temperature applications as it has the ability to retain good metallurgical stability at elevated temperatures. Due to the nature of the applications used for, parts made from Incoloy 800 usually require different machining processes such as milling and turning. Therefore, the current study aims to investigate the milling performance of Incoloy 800 under different cutting parameters (75–150 m/min and 0.075–0.15 mm/rev) and cooling conditions namely dry, flood, Minimum Quantity Lubrication (MQL) and Cryogenic (Cryo)+MQL. It was observed that all machinability metrics improved in the MQL+Cryo C/L environment. It is noticeable that the surface roughness value improved by 30% in this environment. In addition, a model based on artificial neural networks (ANN) and particle swarm optimization (PSO) was proposed to analyze the results and predict optimum cutting parameters. It appears that Cryo+MQL strategies are the best option for all cutting parameters. It was found that the estimations for surface roughness, flank wear, and cutting temperature with the proposed ANN architecture are achieved with overall relative error of 6.08%, 12.38%, and 8.32%, respectively. The proposed model resulted in good performance between the experimental test data and the predicted values. The developed model made the most efficient predictions for the MQL+Cryo cutting environment. It was observed that the estimations of the different input parameters in the MQL+Cryo cutting environment present a relative error of 8.36%, 1.46%, and 2.38% for surface roughness, flank wear, and cutting temperature, respectively. By utilizing the predictive capability of the trained ANN model, the optimization of the input parameters was carried out with the PSO technique. Thus, with the developed PSO-ANN model, promising findings were obtained in overcoming important handicaps such as time and cost in experimental studies.
Yazarlar (6)
1
Emine Şap
ORCID: 0000-0002-7739-0655
2
Üsame Ali Usca
3
Serhat Şap
ORCID: 0000-0001-5177-4952
4
Hasan Polat
5
Khaled Giasin
6
Mete Kalyoncu
ORCID: 0000-0002-2214-7631
Anahtar Kelimeler
Artificial intelligent
Hybrid cooling
Incoloy 800
LN 2
Machining
MQL
Kurumlar
Bingöl Üniversitesi
Bingol Turkey
Konya Technical University
Konya Turkey
University of Portsmouth
Portsmouth United Kingdom
Metrikler
11
Atıf
6
Yazar
6
Anahtar Kelime