scholarly journals Effect of Minimum Quantity Lubrication on Surface Roughness and Temperature in Milling of EN31 Steel for Die Making

2019 ◽  
Vol 69 (1) ◽  
pp. 61-68
Author(s):  
Bhosetty Keerthana ◽  
Gurram Vijaya Kumar ◽  
Kumba Anand Babu
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Tool Material ◽  
Minimum Quantity Lubrication ◽  
Depth Of Cut ◽  
Swarm Optimization ◽  
Minimum Quantity ◽  
Particle Swarm Optimization Technique

AbstractMinimum Quantity Lubrication has enormous influence on the process parameters in machining. The main aim of the present work is to study the effects of spindle speed, depth of cut, tool material, amount of coolant dispensed and type of coolant on surface roughness and tool temperature in EN31 steel die making including Minimum Quantity Lubrication (MQL) by introducing a self-designed MQL setup and to optimize the responses using fuzzy-logic and Particle Swarm Optimization technique.

Particle Swarm Optimization and Machinability Aspects during Turning of Hardened D3 Steel

2020 ◽  
Vol 19 (04) ◽  
pp. 641-662
Author(s):  
Debabrata Rath ◽  
Sumanta Panda ◽  
Ankan Mishra ◽  
Kamal Pal
Keyword(s):  
Particle Swarm Optimization ◽  
Cutting Force ◽  
Removal Rate ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Swarm Optimization ◽  
Machined Surface Quality ◽  
Particle Swarm Optimization Technique

Metal cutting processes are associated with excessive forces, friction and heat generation due to continuous intensive contact between the active cutting tools and the work which degrades the machined surface quality. The hardened tool steel having excellent wear resistance property receives an extensive promotion, investigation and application in the die manufacturing industries. In this research, the machinability of hardened AISI D3 tool steel has been investigated using TiN-coated Al2[Formula: see text](C,N) ceramic tool inserts as per Taguchi’s [Formula: see text] orthogonal design of experiments is in horizontal turning under the dry condition. The experimental dataset was used for the regression model development of primary process outputs as well as mean cutting force using the response surface methodology (RSM) which was found to be significant. The parametric interaction effects on each process output were studied along with with chip morphology in detail. The cutting force as well as material removal rate was found to be significantly influenced by depth of cut, whereas machined surface quality was primarily dependent on tool feed rate. The wider with less prominent saw tooth chips got changed to narrower saw-tooth form with intensive shear bands at higher feed rates. Finally, an attempt has been made to develop an optimal parametric setting using particle swarm optimization technique to achieve minimum surface roughness and maximum material removal rate at less cutting force. The optimal parametric setting was high cutting speed (308[Formula: see text]m/min), high tool feed rate (0.08[Formula: see text]mm/rev) with medium depth of cut (0.6[Formula: see text]mm) to achieve each objective. This evolutionary particle swarm optimization technique was found to be highly accurate (within 8% error) as per validation experiment.

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