Multi-objective Optimization of Machining Parameters in µ-EDM Drilling of SS317L Using Novel JAYA and TLBO Algorithms

2022 ◽  
pp. 1363-1376
Author(s):  
V. Rajashekar ◽  
Shivraj Narayan Yeole
Keyword(s):  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Edm Drilling

Parametric optimization in modified air abrasive jet machining

2019 ◽  
Vol 15 (3) ◽  
pp. 617-629
Author(s):  
S. Rajendra Prasad ◽  
K. Ravindranath K. Ravindranath ◽  
M.L.S. Devakumar M.L.S. Devakumar
Keyword(s):  
Optimization Technique ◽  
Mesh Size ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Abrasive Jet Machining ◽  
Alloy Material ◽  
Size Measure ◽  
First Time

Purpose The choice of best machining parameters is an extremely basic factor in handling of any machined parts. The purpose of this paper is to exhibit a multi-objective optimization technique; in view of weighted aggregate sum product assessment (WASPAS) technique toward upgrade the machining parameters in modified air abrasive jet machining (MAAJM) process: injecting pressure, stand-off distance (SOD), and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Three conflicting destinations, material removal rate (MRR), surface roughness (SR) and taper angles (Ta), respectively, are considered at the same time. The proposed procedure uses WASPAS, which is the examination of parametric optimization of the abrasive jet machining (AJM) process. The results was used any scopes of reactions in MAAJM process is the ideal setting of parameters are resolved through investigations represented. There is wide utilization of Nickel 233 in aviation enterprises; machining information on producing a hole utilizing MAAJM for the first time is given in this work, which will be helpful different industries. Design/methodology/approach This paper exhibits a multi-objective optimization technique; in view of WASPAS technique toward upgrade the machining parameters in MAAJM process: injecting pressure, SOD, and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Findings As an outcome of using the tool in any ranges of responses in the AJM process, the optimal setting of parameters is determined through experiments illustrated. The machining data of generating a hole using AJM are studied for the first time in this work, which will be useful for aerospace industries, where Nickel 233 is used broadly. Originality/value A new material in unconventional machining process and also a multi-objective optimization technique are adopted.

Multi-Objective Optimization of Electrical Discharge Machining Parameters for 2024 Aluminum Alloy Using Grey-Taguchi Method

2020 ◽  
Vol 998 ◽  
pp. 55-60
Author(s):  
Jurapun Phimoolchat ◽  
Apiwat Muttamara
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
2024 Aluminum Alloy ◽  
Multi Objective ◽  
Effect Estimation ◽  
Voltage Performance ◽  
Pulse On Time

This paper focused on Grey relational analysis (GRA) to optimize EDM parameters through multi-objective optimization for Al2024 aluminum and electrode graphite ISO-63 was used as a cutting tool. The process parameters pulse on time, duty factor, pulse current and open voltage. Performance characteristics examined included material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Taguchi’s 27 experimental designs, often called an orthogonal array (OA), was utilized to ignore interaction and concentrate on main effect estimation. GRA was performed to optimize input parameters levels. Results were that MRR increased from 35.00 to 35.11 mm3/min, EWR decreased from 11.63 to 10.89 mm3/min, and SR decreased from 5.01 to 4.97 μm. Taguchi and GRA resulted in clear improvements in MRR, EWR, and SR.

Multi Objective Optimization of Cutting Parameters in Machining Cellulose Based Hybrid Composites

2015 ◽  
Vol 761 ◽  
pp. 287-292
Author(s):  
Raja Izamshah ◽  
Zainudin Zuraidah ◽  
Mohd Shahir Kasim ◽  
M. Hadzley ◽  
M. Amran
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Hybrid Composites ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Machining Performance ◽  
Machined Surface ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Cellulose based hybrid composites are gaining popularity in the growing green communities. With extensive studies and increasing applications for future advancement, the need for an accurate and reliable guidance in machining this type of composites has increased enormously. Smooth and defect free machined surface are always the ultimate objectives. The present work deals with the study of machining parameters (i.e. spindle speed, feed rate and depth of cut) and their effects on machining performance (i.e. surface roughness and delamination) to establish an optimized setup of machining parameters in achieving multi objective machining performance. Cellulose based hybrid composites consist of jute (a bast fiber) and glass fiber embedded in polyester resins. Response Surface Methodology (RSM) using Box-Behnken Design (BBD) was chosen as the design of experiment approach for this study. Based on that experimental approach, 17 experimental runs were conducted. Mathematical model for each response was developed based on the experimental data. Adequacy of the models were analyzed statistically using Analysis of Variance (ANOVA) in determining the significant input variables and possible interactions. The multi objective optimization was performed through numerical optimization, and the predicted results were validated. The agreement between the experimental and selected solution was found to be strong, between 95% to 96%, thus validating the solution as the optimal machining condition. The findings suggest that feed rate was the main factor affecting surface roughness and delamination .

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.

scholarly journals A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2616
Author(s):  
Lijun Song ◽  
Jing Shi ◽  
Anda Pan ◽  
Jie Yang ◽  
Jun Xie
Keyword(s):  
Energy Consumption ◽  
Particle Swarm ◽  
Economic Benefits ◽  
Machining Parameters ◽  
Manufacturing Companies ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Multi Objective ◽  
Use Of Resources ◽  
Machining Operations

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

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