EconPapers    
Economics at your fingertips  
 

MODELING AND ANALYSIS OF MACHINING PARAMETERS OF ECAP PROCESSED COMMERCIAL PURE ALUMINUM USING RSM AND ANN

S. Surendarnath, T. Ramachandran and B. Ravisankar
Additional contact information
S. Surendarnath: Department of Mechanical Engineering, Sri Venkateswara College of Engineering & Technology (A), Chittoor 517127, Andhra Pradesh, India
T. Ramachandran: ��Department of Mechanical Engineering, Jain University, Bangalore 562112, Karnataka, India
B. Ravisankar: ��Department of Metallurgical and Materials Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli 620015, Tamilnadu, India

Surface Review and Letters (SRL), 2022, vol. 29, issue 02, 1-13

Abstract: Equal channel angular pressing (ECAP) processed materials have higher grain refinement and strength, and they exhibit more surface roughness when they are machined. This enhancement in the properties highly influences the surface roughness and material removal rate of the materials. The commercial pure aluminum has a wide variety of applications when it is enhanced with high strength properties. In this paper, the machinability of commercially pure aluminum processed through ECAP is investigated in turning operations. Different ECAP processes are carried out to study the microstructural characterization and mechanical properties of the material. The material removal rate and surface roughness are tested by performing the turning operation in the CNC lathe with chemical vapor deposited carbide tool such that the feed rate, spindle speed and depth of cut are considered as the machining variables. To create a hypothesis for the experimentation, the empirical models are developed for the objective functions using the statistical technique response surface methodology (RSM) such that the response models are the objective functions and the model variables are the machining parameters. The response models are verified for the adequacy through ANOVA and p-test, and also verified for the closeness with the experimental results. Artificial neural network (ANN)-based empirical equations are also developed for the objective functions using the RSM design matrix and the results of both the RSM and ANN are compared for the suitability.

Keywords: ECAP; CVD; pure aluminum; machinability; RSM; ANOVA; ANN (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218625X22500251
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:srlxxx:v:29:y:2022:i:02:n:s0218625x22500251

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218625X22500251

Access Statistics for this article

Surface Review and Letters (SRL) is currently edited by S Y Tong

More articles in Surface Review and Letters (SRL) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().

 
Page updated 2025-03-20
Handle: RePEc:wsi:srlxxx:v:29:y:2022:i:02:n:s0218625x22500251