Influence of Cutting Fluid-Based CuO-Nanofluid with Boric Acid-Nanoparticles Additives on Machining Performances of AISI 4340 Tool Steel in High-Speed Turning Operation

2021 ◽  
Author(s):  
Mohammad Jafarian Zenjanab ◽  
Siamak Pedrammehr ◽  
Mohammad Reza Chalak Qazani ◽  
Mohammad Reza Shabgard
Keyword(s):  
Boric Acid ◽  
Tool Steel ◽  
High Speed ◽  
Cutting Fluid ◽  
Turning Operation ◽  
High Speed Turning ◽  
Aisi 4340

PROCESSING AND CHARACTERIZATION OF ECO-FRIENDLY CUTTING FLUID WITH NANO ADDITIVES FOR TURNING OPERATION

2021 ◽  
pp. 2150057
Author(s):  
M. K. MARICHELVAM ◽  
S. SENTHIL MURUGAN ◽  
K. MAHESWARAN ◽  
D. SHYAMPRASAD VARMA
Keyword(s):  
Boric Acid ◽  
Environmental Sustainability ◽  
Coconut Oil ◽  
Cutting Fluid ◽  
Interface Temperature ◽  
Test Flash ◽  
Cutting Fluids ◽  
Machined Surface ◽  
Turning Operation

Machining quality depends on numerous factors such as speed, feed rate, quality of the materials, the cutting fluids used and so on. The quality of machining components can also be improved by using appropriate cutting fluids. In this study, the three different types of eco-friendly cutting fluids based on coconut oil with nano boric acid particles were synthesized with nanoadditives and characterized during the lathe-turning operation of mild steel. The obtained results were compared between the dry/plain turning (without the cutting fluid) and the turning with the cutting fluids like coconut oil and mineral oil with nanoparticles. In industries, a wide variety of cutting fluids are used; however, most of these cutting fluids are made up of synthetic materials which may affect the environment significantly. Hence, it is essential to develop eco-friendly cutting fluids for environmental sustainability. Here, the cutting fluids were characterized by the morphological study on nanoparticles (400[Formula: see text]nm) and the machined surface using scanning electron microscope (SEM), viscosity test, flash and fire point, surface roughness on machined part, tool tip-workpiece interface temperature, cutting force and flank wear measurement. The results showed that cutting fluids with 0.5% of boric acid had better performance.

Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

2011 ◽  
Vol 314-316 ◽  
pp. 341-345
Author(s):  
Bo Di Cui
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Tool Steel ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
System Modelling ◽  
Inference System ◽  
High Speed Turning ◽  
Neuro Fuzzy

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

Sign in / Sign up

Export Citation Format

Share Document