*Result*: Predictive Modeling of Surface Integrity and Material Removal Rate in Computer Numerical Control Machining: Effects of Thermal Conductivity and Hardness.

Title:
Predictive Modeling of Surface Integrity and Material Removal Rate in Computer Numerical Control Machining: Effects of Thermal Conductivity and Hardness.
Authors:
Alsoufi MS; Department of Mechanical Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah 21955, Saudi Arabia., Bawazeer SA; Department of Mechanical Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
Source:
Materials (Basel, Switzerland) [Materials (Basel)] 2025 Mar 29; Vol. 18 (7). Date of Electronic Publication: 2025 Mar 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101555929 Publication Model: Electronic Cited Medium: Print ISSN: 1996-1944 (Print) Linking ISSN: 19961944 NLM ISO Abbreviation: Materials (Basel) Subsets: PubMed not MEDLINE
Imprint Name(s):
Publication: May 2010- : Basel, Switzerland : MDPI
Original Publication: Basel, Switzerland : Molecular Diversity Preservation International, 2008-2010.
References:
Materials (Basel). 2024 Apr 16;17(8):. (PMID: 38673183)
Contributed Indexing:
Keywords: CNC turning; hardness; material removal rate (MRR); predictive modeling; surface roughness (Ra); surface waviness (Wa); thermal conductivity
Entry Date(s):
Date Created: 20250424 Latest Revision: 20250426
Update Code:
20260130
PubMed Central ID:
PMC11990786
DOI:
10.3390/ma18071557
PMID:
40271775
Database:
MEDLINE

*Further Information*

*This study investigates the influence of thermal conductivity and hardness on computer numerical control (CNC) turning performance, focusing on key machining metrics-material removal rate (MRR), surface roughness ( R<subscript>a</subscript> ), and surface waviness ( W<subscript>a</subscript> )-across five engineering materials: aluminum 6061, brass C26000, bronze C51000, carbon steel 1020, and stainless steel 304. Experimental results reveal a strong correlation between material properties and machining efficiency. Materials with high thermal conductivity (>100 W/m·K) exhibited up to 38% higher MRR and improved surface integrity compared to low-conductivity counterparts. Aluminum 6061 achieved the highest MRR (7.5 mm <sup>3</sup> /min at a 0.25 mm/rev feed rate), with the lowest R<subscript>a</subscript> (~0.58 µm) and W<subscript>a</subscript> (~0.4576 µm), confirming its excellent machinability and heat dissipation. Conversely, stainless steel 304, characterized by low thermal conductivity (16 W/m·K) and high hardness (210 HBW), recorded the lowest MRR (1.125 mm <sup>3</sup> /min), elevated R<subscript>a</subscript> (>1.0 µm), and substantial waviness ( W<subscript>a</subscript> ~0.9442 µm), indicating severe tool wear and thermal deformation. A multivariable regression model incorporating cutting speed, feed rate, thermal conductivity, and hardness was developed to predict MRR, achieving high predictive accuracy ( R<sup>2</sup> > 0.92) for high-conductivity materials. Deviations of ±0.5 mm <sup>3</sup> /min were observed in harder, low-conductivity materials due to nonlinear effects such as strain hardening and thermal expansion. Measurement uncertainty analysis, with an estimated expanded uncertainty of ±2.5% for MRR and ±0.02 µm for surface metrics, ensures the reliability of these findings. These results underscore the importance of material-specific machining parameter optimization to enhance productivity, surface quality, and tool longevity in high-precision industries, including aerospace, automotive, and biomedical manufacturing.*