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Showing 2 results for Hard Turning
Prediction of surface roughness using a novel approach
M Kaladhar, Vss Sameer Chakravarthy, Psr Chowdary,
Volume 32, Issue 3 (9-2021)
Abstract
Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining parameters. Consequently, enumerating the good relation between surface roughness (Ra) and machining parameters is a highly focused task. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness in hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). When compared with RSM models, the proposed FPA based model showed the least percentage of mean absolute error. The model obtained the strongest correlation coefficient value of 99.75% among the other models values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is a better choice over the RSM based regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.
Influence of Al2O3/MoS2 hybrid nanofluid MQL technique on cutting forces and surface roughness in hard turning using CBN inserts
Tuan Ngo, Bao Ngoc Tran, Minh Duc Tran, the Long Tran, Trang Dang,
Volume 35, Issue 4 (12-2024)
Abstract
Improving hard machining efficiency is a growing concern in industrial production, but environmentally friendly characteristics are guaranteed. Nanofluid minimum quantity lubrication (NF MQL) has emerged as a promising solution to improve cooling and lubrication performance in the cutting zone. This paper utilizes Box-Behnken experimental design to identify the influences of Al2O3/MoS2 hybrid nanofluid MQL hard turning using CBN inserts on surface roughness and cutting forces. Mathematical models were employed to predict thrust cutting force, tangential cutting force, and surface roughness in hard turning under MQL conditions using Al2O3/MoS2 hybrid nanofluid. The study results reveal that the minimum thrust force (Fy) occurs at a nanoparticle concentration of 0.5%, air pressure of 5 bar, and flow rate of 236 l/min. In comparison, the tangential force (Fz) reaches its minimum at a nanoparticle concentration of 0.8%, air pressure of 5 bar, and airflow rate of 227 l/min. The minimum surface roughness was achieved with a nanoparticle concentration of 1%, air pressure of 4.7 bars, and airflow rate of 186 l/min. Additionally, based on the multi-objective optimization, an optimal parameter set of NC=1%, p=5 bar, and Q = 210 l/min was identified to bring out the minimal values of surface roughness (Ra) of 0.218 µm, thrust force (Fy) of 115.9 N, and tangential force (Fz) of 93.3 N.

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