scholarly journals Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5109
Author(s):  
Milan Joshi ◽  
Ranjan Kumar Ghadai ◽  
S. Madhu ◽  
Kanak Kalita ◽  
Xiao-Zhi Gao
Keyword(s):  
Manufacturing Industry ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Process ◽  
Micro Milling ◽  
Micro Machining ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Micro Turning

The popularity of micro-machining is rapidly increasing due to the growing demands for miniature products. Among different micro-machining approaches, micro-turning and micro-milling are widely used in the manufacturing industry. The various cutting parameters of micro-turning and micro-milling has a significant effect on the machining performance. Thus, it is essential that the cutting parameters are optimized to obtain the most from the machining process. However, it is often seen that many machining objectives have conflicting parameter settings. For example, generally, a high material removal rate (MRR) is accompanied by high surface roughness (SR). In this paper, metaheuristic multi-objective optimization algorithms are utilized to generate Pareto optimal solutions for micro-turning and micro-milling applications. A comparative study is carried out to assess the performance of non-dominated sorting genetic algorithm II (NSGA-II), multi-objective ant lion optimization (MOALO) and multi-objective dragonfly optimization (MODA) in micro-machining applications. The complex proportional assessment (COPRAS) method is used to compare the NSGA-II, MOALO and MODA generated Pareto solutions.

Multi-Objective Optimization for the Micro-Milling Process With Adaptive Data Modeling

2011 ◽  
Author(s):  
Xinyu Liu ◽  
Weihang Zhu ◽  
Victor Zaloom
Keyword(s):  
Surrogate Model ◽  
Data Modeling ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Study ◽  
Machining Parameter

This paper presents a multi-objective optimization study for the micro-milling process with adaptive data modeling based on the process simulation. A micro-milling machining process model was developed and verified through our previous study. Based on the model, a set of simulation data was generated from a factorial design. The data was converted into a surrogate model with adaptive data modeling method. The model has three input variables: axial depth of cut, feed rate and spindle speed. It has two conflictive objectives: minimization of surface location error (which affects surface accuracy) and minimization of total tooling cost. The surrogate model is used in a multi-objective optimization study to obtain the Pareto optimal sets of machining parameters. The visual display of the non-dominated solution frontier allows an engineer to select a preferred machining parameter in order to get a lowest cost solution given the requirement from tolerance and accuracy. The contribution of this study is to provide a streamlined methodology to identify the preferred best machining parameters for micro-milling.

A Simple GA Based Approach for Multi-Objective Optimization of Machining Parameters

2014 ◽  
Vol 974 ◽  
pp. 402-407 ◽  
Author(s):  
Akhtar Waseem ◽  
Jian Fei Su ◽  
Wu Yi Chen ◽  
Peng Fei Sun
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Simple Approach ◽  
Machining Parameters ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox ofMATLABis used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.

scholarly journals Multi-Objective Optimization Model of Multi-Pass Turning Operations to Minimize Energy, Carbon Emissions, and Production Costs

2020 ◽  
Vol 21 (2) ◽  
pp. 213-224
Author(s):  
Aprilia Dityarini ◽  
Eko Pujiyanto ◽  
I Wayan Suletra
Keyword(s):  
Carbon Emissions ◽  
Optimization Model ◽  
Sustainable Manufacturing ◽  
Cutting Parameters ◽  
Production Costs ◽  
Machining Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Decision Variables ◽  
Optimal Values

Sustainable manufacturing aspects are environmental, economic, and social. These aspects can be applied to an optimization model in the machining process. An optimization model is needed to determine the optimum cutting parameters. This research develops a multi-objective optimization model that can optimize cutting parameters on a multi-pass turning. Decision variables are cutting parameters multi-pass turning. This research has three objective functions for minimizing energy, carbon emissions, and costs. Three functions are searched for optimal values using the GEKKO.  A numerical example is given to show the implementation of the model and solved using GEKKO and Interior Point Optimizer (IPOPT). The results of optimization indicate that the model can be used to optimize the cutting parameters.

Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718

2019 ◽  
Vol 71 (6) ◽  
pp. 787-794 ◽  
Author(s):  
Xiaohong Lu ◽  
FuRui Wang ◽  
Liang Xue ◽  
Yixuan Feng ◽  
Steven Y. Liang
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Cutting Parameter ◽  
Multi Objective

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.

Optimization of Process Parameters Using Taguchi Coupled Genetic Algorithm

2017 ◽  
pp. 67-93 ◽  
Author(s):  
Supriyo Roy ◽  
J. Paulo Davim ◽  
Kaushik Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Nsga Ii ◽  
Optimization Of Process Parameters ◽  
Cnc Lathe

In the era of mass manufacturing, Material removal rate and Surface Roughness are of primary concern even in manufacturing using contemporary CNC machines. In this work, L27 Orthogonal Array of Taguchi method is selected for three parameters (Depth of cut, Feed and Speed) with three different levels to optimize the turning parameters for Material Removal Rate and Surface Roughness on an EMCO Concept Turn 105 CNC lathe for machining SAE 1020 material with carbide tool. The MRR and SR are observed as the objective to develop the combination of optimum cutting parameters. The objectives were optimized using Taguchi, Grey Taguchi and NSGA-II. The result from these techniques was compared to identify the optimal values of cutting parameters for maximum MRR, minimum SR and best combination of both. This study also produced a predictive equation for determining MRR and SR for a given set of parameters outside the considered values. Thus, with the proposed optimal parameters it is possible to increase the efficiency of machining process and decrease production cost in CNC Lathe.

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