scholarly journals MULTI OBJECTIVE OPTIMIZATION OF FSW PROCESS PARAMETERS USING GENETIC ALGORITHM AND TLBO ALGORITHM

2020 ◽  
Vol 15 (7) ◽  
Author(s):  
Lam Suvarna Raju
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tlbo Algorithm

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

A hybrid multi-objective optimization of 3D printing process parameters using genetic algorithm

2021 ◽  
Author(s):  
Zahoor Ahmed Shariff ◽  
Lokesh M. ◽  
K. Mayandi ◽  
A. K. Saravanan ◽  
P. Sethu Ramalingam ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
3D Printing ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Printing Process ◽  
Multi Objective

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

scholarly journals RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm

Metals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1003 ◽  
Author(s):  
Xiao Xiao ◽  
Jin-Jae Kim ◽  
Myoung-Pyo Hong ◽  
Sen Yang ◽  
Young-Suk Kim
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Sheet Forming ◽  
Incremental Sheet Forming ◽  
Thickness Reduction ◽  
Multi Objective Optimization ◽  
Minimum Thickness ◽  
Multi Objective ◽  
Forming Angle ◽  
Tool Diameter

In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.

MULTI-OBJECTIVE OPTIMIZATION OF EDM PROCESS PARAMETERS FOR MACHINING OF HYBRID ALUMINUM METAL MATRIX COMPOSITES (Al7075/TiC/B4C) USING GENETIC ALGORITHM

2019 ◽  
Vol 26 (10) ◽  
pp. 1950071 ◽  
Author(s):  
K. PONAPPA ◽  
K. S. K. SASIKUMAR ◽  
M. SAMBATHKUMAR ◽  
M. UDHAYAKUMAR
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Pulse Current ◽  
Metal Removal ◽  
Removal Rate ◽  
Optimum Process ◽  
Multi Objective Optimization ◽  
Influence Of Process Parameters ◽  
Scanning Electron Microscope Study ◽  
Multi Objective

This study deals with the investigation on the effect of Electrical Discharge Machining (EDM) parameters during machining of hybrid composite (Al 7075/TiC/B4C). The optimum process parameters of die sinking EDM like pulse current, pulse duration and gap voltage on metal removal rate, tool wear rate and surface finish were investigated. Full factorial experimental design was selected for experiments. Analysis of variance was employed to study the influence of process parameters and their interactions on response variables. Among the process parameters considered, it was observed that the pulse current was found to be more influential in affecting MRR, TWR and SR. The other parameters have little effect on the response variable. Multi-objective optimization study was also performed using genetic algorithm to find the optimum parameter setting for controversial objective function combination such as high MRR and low SR and High MRR and low TWR. Scanning electron microscope study was performed to study the surface characteristics.

scholarly journals Modeling and optimization of machining parameters in milling of INCONEL-800 super alloy considering energy, productivity, and quality using nanoparticle suspended lubrication

2020 ◽  
pp. 002029402092584
Author(s):  
Te-Ching Hsiao ◽  
Ngoc-Chien Vu ◽  
Ming-Chang Tsai ◽  
Xuan-Phuong Dang ◽  
Shyh-Chour Huang
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Specific Cutting Energy ◽  
Multi Objective Optimization ◽  
Energy Productivity ◽  
Multi Objective ◽  
Cutting Energy ◽  
Super Alloy

Inconel-800 super alloy is a newly difficult-to-cut material. To improve the cutting conditions for this metal, sustainable methods in which minimum quantity lubrication enhanced with suspended nanoparticle were employed. This work also aims to model the relationship between process parameters (cutting speed, feed per tooth, depth of cut, and corner radius of cutting tool) and machining responses (surface roughness, specific cutting energy, cutting power, and material removal rate) using orthogonal array design of experiment and response surface methodology. Non-dominated sorting genetic algorithm was used to solve the multi-objective optimization problems in terms of energy, productivity, and quality of the machining process. The results indicate that the application of the response surface methodology model in combination with non-dominated sorting genetic algorithm is appropriate for this study due to the goodness of fit of response surface methodology and the global optimum solution of genetic algorithm. Because multi-objective optimization gives multiple solutions, Pareto plot and data mining are employed to support the selection of process parameters that can save time and cost and increase energy efficiency, meanwhile, simultaneously improve productivity and surface quality. The research results show that the specific cutting energy and energy consumption can be reduced up to 20.2% and 6.4%, respectively.

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