Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718

Purpose The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718. Design/methodology/approach Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. Findings This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718. Originality/value There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

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Evaluation and multi-objective optimization of nose wear, surface roughness and cutting forces using grey relation analysis (GRA)

Journal of the Brazilian Society of Mechanical Sciences and Engineering ◽

10.1007/s40430-019-2057-4 ◽

2019 ◽

Vol 41 (12) ◽

Cited By ~ 2

Author(s):

Gaurav D. Sonawane ◽

Vikas G. Sargade

Keyword(s):

Surface Roughness ◽

Wear Surface ◽

Cutting Forces ◽

Multi Objective Optimization ◽

Grey Relation Analysis ◽

Multi Objective ◽

Relation Analysis ◽

Grey Relation

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Multi-Objective Optimization of Surface Roughness and Cutting Forces in Hard Milling Using Taguchi and Response Surface Methodology

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.773.220 ◽

2018 ◽

Vol 773 ◽

pp. 220-224 ◽

Cited By ~ 4

Author(s):

Ngoc Chien Vu ◽

Shyh Chour Huang ◽

Huu That Nguyen

Keyword(s):

Surface Roughness ◽

Response Surface Methodology ◽

Taguchi Method ◽

Response Surface ◽

Cutting Forces ◽

Machining Process ◽

Process Conditions ◽

Multi Objective Optimization ◽

Hard Milling ◽

Multi Objective

Cutting forces and surface roughness are important output parameters affecting the machining performance and quality of any machined surface in hard milling. In order to obtain the best surface quality and highest productivity, the input-cutting parameters need be considered and chosen properly whenever hard milling is involved. Therefore, in this paper, an attempt is made to conduct the multi-objective optimization of the surface roughness (Ra) and the resultant cutting force (Ft) in hard milling of SKD61 steel by Taguchi method and Response Surface Methodology (RSM). Values of the input parameters for milling tests are chosen through the stability lobe diagram of a machine tool simulated by the use of Cutpro software. The Taguchi method is used for designing all of the milling experiments. The values of Ra and Ft are measured by a Surftest SJ-400 and a dynamometer, respectively, and then analysis of variance is conducted to find out the effect of machining process conditions on Ra and Ft. In order to get the low Ft and Ra, a multi-objective optimization is implemented with the use of the desirability function. The results reveal that the optimized machining conditions for Ra and Ft are a cutting speed of 100 m/min, a feed rate of 0.015 mm/tooth, and a depth of cut of 0.44 mm, with predicted Ra of 0.206 µm and Ft of 66.58 N.

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Analysis, modeling, and multi-objective optimization of machining inconel 718 with nano-additives based minimum quantity coolant

Applied Soft Computing ◽

10.1016/j.asoc.2021.107416 ◽

2021 ◽

pp. 107416

Author(s):

H. Hegab ◽

A. Salem ◽

S. Rahnamayan ◽

H.A. Kishawy

Keyword(s):

Inconel 718 ◽

Multi Objective Optimization ◽

Minimum Quantity ◽

Multi Objective ◽

Nano Additives

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Multi-Objective Optimization Design of an Electrohydrostatic Actuator Based on a Particle Swarm Optimization Algorithm and an Analytic Hierarchy Process

Energies ◽

10.3390/en11092426 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2426 ◽

Cited By ~ 2

Author(s):

Bo Yu ◽

Shuai Wu ◽

Zongxia Jiao ◽

Yaoxing Shang

Keyword(s):

Particle Swarm Optimization ◽

Power Consumption ◽

Analytic Hierarchy Process ◽

Optimization Design ◽

Pareto Front ◽

Multi Objective Optimization ◽

Analytic Hierarchy ◽

Swarm Optimization ◽

Multi Objective ◽

Hierarchy Process

During the last few years, the concept of more-electric aircraft has been pushed ahead by industry and academics. For a more-electric actuation system, the electrohydrostatic actuator (EHA) has shown its potential for better reliability, low maintenance cost and reducing aircraft weight. Designing an EHA for aviation applications is a hard task, which should balance several inconsistent objectives simultaneously, such as weight, stiffness and power consumption. This work presents a method to obtain the optimal EHA, which combines multi-objective optimization with a synthetic decision method, that is, a multi-objective optimization design method, that can combine designers’ preferences and experiences. The evaluation model of an EHA in terms of weight, stiffness and power consumption is studied in the first section. Then, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced to obtain the Pareto front, and an analytic hierarchy process (AHP) is applied to help find the optimal design in the Pareto front. A demo of an EHA design illustrates the feasibility of the proposed method.

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