Palanisamy Angappan

Dr. Palanisamy Angappan

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Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 September 2017 in Journal of Mechanical Science and Technology
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Journal Article
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PAPalanisamy Angappan
Palanisamy Angappan
STSelvaraj Thangiah
Selvaraj Thangiah
S. SivasankaranS. Sivasankaran
S. Sivasankaran

25 April 2018 in Arabian Journal for Science and Engineering
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Journal Article
Closed access
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PAPalanisamy Angappan
Palanisamy Angappan
TST. Selvaraj
T. Selvaraj
SSS. Sivasankaran
S. Sivasankaran

1 January 2018 in Materials Today: Proceedings
Verified
Journal Article
Closed access
PAPalanisamy Angappan
Palanisamy Angappan
TST. Selvaraj
T. Selvaraj

8 January 2021 in Archives of Civil and Mechanical Engineering
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Journal Article
Closed access
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PAPalanisamy Angappan
Palanisamy Angappan
Jeyaprakash., NatarajanJeyaprakash., Natarajan
Jeyaprakash., Natarajan
VSV. Sivabharathi
V. Sivabharathi
S. SivasankaranS. Sivasankaran
S. Sivasankaran


1 January 2025 in Matéria (Rio de Janeiro)
Verified
Journal Article
Open access
UDUmapathi Durai
Umapathi Durai
PDPalanisamy Duraiswamy
Palanisamy Duraiswamy
PAPalanisamy Angappan
Palanisamy Angappan
NMNatarajan Manikandan
Natarajan Manikandan
In this research an effort is made to investigate the machining of Incoloy 800H Super alloy which is hard to machine material due to its rapid hardening property. Laser textured cryogenically treated cutting inserts and vegetable oil enriched with ZnO nano particles through Minimum Quantity Lubrication (MQL) was used. Turning experiments were performed based on L27 Orthogonal Array (OA). Cutting speed, feed rate, and depth of cut were the input parameters. Surface roughness, cutting force, micro hardness, tool-tip temperature, power and material removal rate were the responses measured. Taguchi based Grey Relational Analysis and Analysis of Variance were used to optimize and find the most influencing parameter. The results revealed that the optimal machining conditions were cutting speed at 35 m/min, feed rate at 0.06 mm/rev and depth of cut at 0.5 mm for the multiple-characteristic performances and it was improved by 4.87%. Microhardness, Tool-tip temperature and Power were reduced during machining to 1.31%, 4.53% and 2.10% respectively. Moreover, ZnO nanoparticles enriched base fluid gives better performances in terms of surface roughness, tool wear and cutting force. Furthermore, the White Light Interferometer (WLI) and Scanning Electron Microscopy (SEM) were used to study the machined surface topography and tool wear analysis.