Impact on Machining of AISI H13 Steel Using Coated Carbide Tool under Vegetable Oil Minimum Quantity Lubrication

2019 ◽  
Vol 8 (1) ◽  
pp. 20190154 ◽  
Author(s):  
R. Vishal ◽  
K. Nimel Sworna Ross ◽  
G. Manimaran ◽  
B. K. Gnanavel

Author(s):  
R. Suresh ◽  
Ajith G. Joshi

Hard turning with multilayer coated carbide tool has several benefits over grinding process such as, reduction of processing cost and increased productivity. The objective was to establish a correlation between cutting parameters with cutting force, tool wear and surface roughness on workpiece. In the present study, machinability of AISI H13 steel with TiC/TiCN/Al2O3 coated carbide tool using statistical techniques. An attempt has been made to analyze the effects of process parameters on machinability aspects using design of experiments. Response surface plots are generated for the study of interaction effects of cutting conditions on machinability factors. The obtained results revealed that, the optimal combination of low feed rate and low depth of cut with high cutting speed is beneficial for reducing machining force. The cutting tool wears increases almost linearly with increase in cutting speed and feed rate. The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness.



2021 ◽  
Vol 1034 (1) ◽  
pp. 012099
Author(s):  
Mahros Darsin ◽  
Rika Dwi Hidayatul Qoryah ◽  
Robertus Sidartawan ◽  
Allen Luviandy ◽  
Aris Zainul Muttaqin ◽  
...  


Author(s):  
Lalatendu Dash ◽  
Smita Padhan ◽  
Anshuman Das ◽  
Sudhansu Ranjan Das

The present research addresses the machinability of hardened die steel (AISI D3, 61HRC) in hard turning using multilayer (TiCN/Al2O3/TiN) coated carbide tool under nanofluid based minimum quantity lubrication-cooling condition, where no previous data are available. Power consumption, flank wear, chip morphology and surface integrity (microhardness, residual stress, white layer formation, machined surface morphology, and surface roughness) are considered as technological performance characteristics to evaluate the machinability. Combined approach of central composite design - analysis of variance, response surface methodology and desirability function analysis have been employed respectively for experimental investigation, predictive modelling and multi-response optimization. With a motivational philosophy of “Go Green-Think Green-Act Green”, the work also deals with energy saving carbon footprint analysis and sustainability assessment to recognize the green manufacturing in the context of safer and cleaner production. under environmental-friendly nanofluid assisted minimum quantity lubrication condition. The quantitative analysis revealed that the cutting speed influenced the power consumption during hard machining (75.78%) and flank wear of coated carbide tool (45.67%); feed rate impacted the surface finish of the machined part (68.8%) significantly. Saw tooth shapes of chip produced due to cyclic cracking. Due to low percentage contribution of error (5.32% to Ra, 6.64% to VB, and 7.79% to Pc), a higher correlation coefficient (R2) was obtained with the quadratic regression model, which showed values of 0.9, 0.88 and 0.92 for surface roughness, flank wear, and power consumption, respectively. Optimization with the highest desirability (0.9173) resulted the optimum machining conditions under NFMQL at the cutting speed of 57 m/min, depth of cut 0.1 mm, feed of 0.07 mm/rev, and insert’s nose radius of 0.4 mm. As a result, under NFMQL tool life was improved by 30.8% and 22.6% in respect of flank wear and surface roughness respectively than when machining with MQL technique by adapting the optimum machining condition. Therefore, using hard nanoparticles-reinforced cutting fluid under minimum quantity lubrication condition in practical manufacturing becomes very promising to improve sustainability.







2019 ◽  
Vol 7 (6) ◽  
pp. 411-423
Author(s):  
Quang-Cherng Hsu ◽  
The-Vinh Do ◽  
Thi-Nguyen Nguyen


2019 ◽  
Vol 1 (9) ◽  
Author(s):  
Feng Gong ◽  
Jun Zhao ◽  
Xiuying Ni ◽  
Changxia Liu ◽  
Junlong Sun ◽  
...  


Sign in / Sign up

Export Citation Format

Share Document