A Hybrid Algorithm-Based Comparative Analysis of a Newly Designed Tool Holder During the Machining of Hastelloy-B3 with MQL

This study proposes a novel response surface methodology (RSM) that integrates the mayfly algorithm (MOMA) and multi-objective particle swarm optimization (MOPSO) to enhance the Hastelloy-B3 turning process using a coated carbide insert tool and minimal quantity lubrication. The RSM design was limit...

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Veröffentlicht in:Arabian journal for science and engineering (2011) Jg. 50; H. 4; S. 2691 - 2714
Hauptverfasser: Murali, T., Devendiran, S., Venkatesan, K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025
Springer Nature B.V
Schlagworte:
Algorithms
Carbide tools
Cutting wear
Engineering
Feed rate
Hastelloy (trademark)
Humanities and Social Sciences
Internal flow
Microhardness
multidisciplinary
Multiple objective analysis
Particle swarm optimization
Research Article-Mechanical Engineering
Residual stress
Response surface methodology
Science
Surface roughness
Tool holders
Tool wear
Turning (machining)
External design
Multi-objective mayfly algorithm
Multi-objective particle swarm optimisation
Response surface methodology
Hastelloy-B3
Internal design
ISSN:2193-567X, 1319-8025, 2191-4281
Online-Zugang:Volltext
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Zusammenfassung:This study proposes a novel response surface methodology (RSM) that integrates the mayfly algorithm (MOMA) and multi-objective particle swarm optimization (MOPSO) to enhance the Hastelloy-B3 turning process using a coated carbide insert tool and minimal quantity lubrication. The RSM design was limited by corresponding force, tool wear, temperature, and surface roughness to plan the three turning parts: the cutting velocity, the feed rate, and the cooling strategy. This strategy comprises the external spray rake (MDT 1) and nose side (MDT 2) on the insert, as well as the internal flow top (MDT 3) and bottom (MDT 4). Hence, it established an association between the input variables and responded using the sequential sum of squares for the two-factor interaction (2FI) technique. In addition, it has trained and tested the proposed hybrid algorithm model using experimental data. The optimization results show that removing heat from the chip and tool area makes MDT 4 more important. Compared to RSM-MOMA, the highest improvement in force was 13.65%, temperature was 5.51%, roughness was 49.96%, and tool wear was 24.13%. Additionally, these optimal values improved microhardness at 9.1%, residual stress at 47.2%, and curled diameter at 7.9%. The proposed hybrid algorithm of RSM-MOMA could be used for other machining tasks.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-024-09218-9