Evidence map›Paper›PMID 40596228›Full record

ArticleScientific reports2025

Implementing a novel TOPSIS-sine cosine algorithm-based hybrid optimization in machining medium-hardened steel.

Ramanuj Kumar, Mohammad Rafighi, Oğur İynen, M Prem Kumar Reddy, Shrikant Zade, Mustafa Özdemir, Anish Pandey, Ramanpreet Singh

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ramanuj KumarSchool of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, Odisha, 751024, India.
Mohammad RafighiDepartment of Mechanical Engineering, Başkent University, Ankara, 06490, Türkiye.
Oğur İynenDepartment of Mechanical Engineering, Yozgat Bozok University, 66100, Yozgat, Türkiye.
M Prem Kumar ReddyDepartment of Mechanical Engineering, Aditya University, Surampalem, Andhra Pradesh, 533437, India.
Shrikant ZadeDepartment of Computer Science Engineering, Nagpur Institute of Technology, Nagpur, Maharashtra, 440013, India.
Mustafa ÖzdemirDepartment of Machine and Metal Technology, Yozgat Bozok University, 66100, Yozgat, Türkiye.
Anish PandeySchool of Mechanical Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, Odisha, 751024, India. anish.pandeyfme@kiit.ac.in.
Ramanpreet SinghDepartment of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India. ramanpreet.singh@jaipur.manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machining medium-hardened steel is particularly challenging because of its high strength and wear resistance, which generate excessive cutting temperatures. The elevated temperature contributes to rapid tool wear and negatively impacts surface quality. Optimizing tool selection, coating composition, geometry, and process variables is crucial for enhancing machinability. This study applied a novel hybrid TOPSIS-sine cosine algorithm to evaluate the performance of three chemical vapor deposited (CVD)-coated carbide cutting inserts in turning medium-hard AISI 4340 grade steel, considering the depth of cut (a), cutting speed (V), feed (f) and workpiece hardness as input variables. Experimentally obtained machining responses, namely resultant force (Fr), power consumption (Pc), surface roughness (Ra), and sound level (SL), were analyzed and compared to determine the optimum insert type. Insert type-3 (TiCN-Al2O3-TiN) demonstrated superior performance, achieving a 16.68% and 26.74% lower Ra than insert type-1 and type-2, respectively. Moreover, the optimal parameters for the most favorable insert (type-3) are determined as H = 30 HRC, V = 190 m/min, f = 0.1 mm/rev, and a = 0.2 mm. Workpiece hardness (H) emerged as the most influential factor affecting machining outcomes. This research recommended insert type-3 at optimized cutting conditions to improve machinability and sustainability in turning medium-hard AISI 4340 grade steel.

Indexed as

Coated carbide toolsCutting forcesMedium hardened steelSurface roughnessTOPSIS-sine cosine algorithmTurning

Identifiers

PMID40596228
PMCPMC12219525

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.