Response Surface Method Optimization of Hard Machining with Cryogenically Treated Cutting Tools: Surface Roughness, Cutting Force, Tool Wear, Rietveld Analysis, and Metallurgical Properties

The study consists of two stages: machinability tests and metallographic examinations. In the first stage of the study, response surface methodology (RSM) optimization was performed for the main‐cutting‐force (Fc) and average surface roughness (Ra) values obtained as a result of turning tests. In the second stage, the wear performance of cutting tool tips with and without deep cryogenic treatment was explained with the support of SEM images, EDX, XRD, and microhardness analyses. As a result of ANOVA analysis, it was found that the most effective parameter on Fc was the feed rate, with 78.89%. As a result of RSM‐optimization, the lowest Fc value was found to be 339.99 N. The most effective parameter on Ra was found to be the feed rate at 69.38%. In terms of tool life, 15% and 11% improvements were achieved in uncoated and coated carbide cutting tools, respectively. As a result of Rieltveld's analysis, it was determined that cryogenic treatment increased the carbide percentages in the tools. As a result of microhardness measurements, it was observed that the hardness of uncoated and coated carbide tools increased by 4.6% and 5.15%, respectively, after cryogenic treatment.

Authors

Institutions

Publication Details

Journal
steel research international
Published
2024-12-27
DOI
https://doi.org/10.1002/srin.202400850
Citations
7
Primary Topic
Metal Alloys Wear and Properties
Type
article
Field-Weighted Citation Impact
0.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Response Surface Method Optimization of Hard Machining with Cryogenically Treated Cutting Tools: Surface Roughness, Cutting Force, Tool Wear, Rietveld Analysis, and Metallurgical Properties

Serhat Filiz, Aysun Takmaz, Fuat Kara, Ömer Erkan
7 citations
steel research international
Metal Alloys Wear and Properties
0.82
article

Response Surface Method Optimization of Hard Machining with Cryogenically Treated Cutting Tools: Surface Roughness, Cutting Force, Tool Wear, Rietveld Analysis, and Metallurgical Properties

Serhat Filiz, Aysun Takmaz, Fuat Kara, Ömer Erkan
article en
7 citations

Abstract

The study consists of two stages: machinability tests and metallographic examinations. In the first stage of the study, response surface methodology (RSM) optimization was performed for the main‐cutting‐force (Fc) and average surface roughness (Ra) values obtained as a result of turning tests. In the second stage, the wear performance of cutting tool tips with and without deep cryogenic treatment was explained with the support of SEM images, EDX, XRD, and microhardness analyses. As a result of ANOVA analysis, it was found that the most effective parameter on Fc was the feed rate, with 78.89%. As a result of RSM‐optimization, the lowest Fc value was found to be 339.99 N. The most effective parameter on Ra was found to be the feed rate at 69.38%. In terms of tool life, 15% and 11% improvements were achieved in uncoated and coated carbide cutting tools, respectively. As a result of Rieltveld's analysis, it was determined that cryogenic treatment increased the carbide percentages in the tools. As a result of microhardness measurements, it was observed that the hardness of uncoated and coated carbide tools increased by 4.6% and 5.15%, respectively, after cryogenic treatment.

steel research internationalVol. 96(7)
Çanakkale Onsekiz Mart Üniversitesi (TR), Düzce Üniversitesi (TR)
Openalex Percentile: Top 25%
Metal Alloys Wear and Properties
0.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.