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.

scholarly journals Investigation of the influence of incremental sheet forming process parameters using response surface methodology

2021 ◽  
Vol 118 (4) ◽  
pp. 401
Author(s):  
Belouettar Karim ◽  
Ould Ouali Mohand ◽  
Zeroudi Nasereddine ◽  
Thibaud Sébastien
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Damage Evolution ◽  
Sheet Forming ◽  
Thickness Reduction ◽  
Forming Process ◽  
Influence Of Process Parameters ◽  
Forming Angle ◽  
Tool Diameter

New methods in metal forming are rapidly developing and several forming processes are used to optimize manufacturing components and to reduce cost production. Single Point Incremental Forming (SPIF) is a metal sheet forming process used for rapid prototyping applications and small batch production. This work is dedicated to the investigation of the profile geometry and thickness evolution of a truncated pyramid. The influence of process parameters during a SPIF process is also studied. A numerical response surface methodology with a Design of Experiments (DOE) is used to improve the thickness reduction and the effects of the springback. A set of 16 tests are performed by varying four parameters: tool diameter, forming angle, sheet thickness, and tool path. The Gurson-Tvergaard-Needleman (GTN) damage model is used to analyze the damage evolution during material deformation. It is found that the model can effectively predict the geometrical profile and thickness with an error of less than 4%. Furthermore, it is noticed that the forming angle is the most influential parameter on the thickness reduction and springback level. Finally, the damage evolution is demonstrated to be sensitive to the forming angle.

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.

Numerical Simulation on the Effect of Process Parameters for Incremental Sheet Forming

2010 ◽  
Vol 97-101 ◽  
pp. 158-161 ◽  
Author(s):  
Qin Qin ◽  
Di Ping Wu ◽  
Mi Li ◽  
Yong Zang
Keyword(s):  
Process Parameters ◽  
Rapid Prototype ◽  
Sheet Forming ◽  
Incremental Sheet Forming ◽  
Layered Manufacturing ◽  
Forming Process ◽  
Step Size ◽  
Flexible Technology ◽  
Vertical Step ◽  
Forming Angle

Incremental sheet forming (ISF), based on the ‘layered manufacturing’ principle of rapid prototype manufacturing technology, is an innovative and highly flexible technology for forming complex shaped parts without the need for costly dies. This paper presents a numerical investigation on the influence of forming process parameters by modeling the forming process. ANSYS/LS-DYNA has been used for the simulation. The results of study show that small vertical step size can improve the accuracy of the forming. Moreover, large forming angle can increase plastic strain and the four screwdown point optimization paths is an effective method to increase the accuracy of the formed sheet.

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.

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