scholarly journals Experimental investigation of cutting parameters influence on surface roughness and cutting forces in hard turning of X38CrMoV5-1 with CBN tool

Sadhana ◽  
2013 ◽  
Vol 38 (3) ◽  
pp. 429-445 ◽  
Author(s):  
H AOUICI ◽  
M A YALLESE ◽  
A BELBAH ◽  
M F AMEUR ◽  
M ELBAH
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Cutting Forces ◽  
Hard Turning ◽  
Cutting Parameters ◽  
Cbn Tool

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

2012 ◽  
Vol 26 (11) ◽  
pp. 3605-3616 ◽  
Author(s):  
Samir Khamel ◽  
Nouredine Ouelaa ◽  
Khaider Bouacha
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Wear Surface ◽  
Cutting Forces ◽  
Hard Turning ◽  
Cbn Tool

An investigation on cutting sound effect on power consumption and surface roughness in CBN tool-assisted hard turning

2021 ◽  
pp. 095440892110580
Author(s):  
Abidin Şahinoğlu ◽  
Mohammad Rafighi ◽  
Ramanuj Kumar
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Hard Turning ◽  
Health And Safety ◽  
Cutting Parameters ◽  
Sound Level ◽  
Sound Emission ◽  
Sound Effect ◽  
Cutting Power ◽  
Cbn Tool

In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

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