Modeling and Optimization of Surface Roughness in Incremental Sheet Forming using a Multi-objective Function

2014 ◽  
Vol 29 (7) ◽  
pp. 808-818 ◽  
Author(s):  
Zhaobing Liu ◽  
Sheng Liu ◽  
Yanle Li ◽  
Paul Anthony Meehan
2018 ◽  
Vol 133 ◽  
pp. 1014-1020 ◽  
Author(s):  
Ajay Kumar ◽  
Vishal Gulati ◽  
Parveen Kumar

Author(s):  
Shamik Basak ◽  
K Sajun Prasad ◽  
Amarjeet Mehto ◽  
Joy Bagchi ◽  
Y Shiva Ganesh ◽  
...  

Prototyping through incremental sheet forming is emerging as a latest trend in the manufacturing industries for fabricating personalized components according to customer requirement. In this study, a laboratory scale single-point incremental forming test setup was designed and fabricated to deform AA6061 sheet metal plastically. In addition, response surface methodology with Box–Behnken design technique was used to establish different regression models correlating input process parameters with mechanical responses such as angle of failure, part depth per unit time and surface roughness. Correspondingly, the regression models were implemented to optimize the input process parameters, and the predicted responses were successfully validated at the optimal conditions. It was observed that the predicted absolute error for angle of failure, part depth per unit time and surface roughness responses was approximately 0.9%, 4.4% and 6.3%, respectively, for the optimum parametric combination. Furthermore, the post-deformation responses from an optimized single point incremental forming truncated cone were correlated with microstructural evolution. It was observed that the peak hardness and highest areal surface roughness of 158 ± 9 HV and 1.943 μm, respectively, were found near to the pole of single-point incremental forming truncated cone, and the highest major plastic strain at this region was 0.80. During incremental forming, a significant increase in microhardness occurred due to grain refinement, whereas a substantial increase in the Brass and S texture component was responsible for the increase in the surface roughness.


Author(s):  
Manish Oraon ◽  
Manish Kumar Roy ◽  
Vinay Sharma

Incremental sheet forming (ISF) is an emerging technique of sheet metal working that comes into the picture in the last two decades. The ISF involved the forming of shapes without using the dedicated dies. ISF is suitable for customized products, rapid prototyping, and low batch production. The study aims to investigate the effect of process parameters on the surface roughness. The experiments are conducted on aluminum AA3003-O grade with six parameters, and the trials are performed according to the design of experiment (DOE). The atomic force microscopy (AFM) technique is used for measuring the surface roughness. Analysis of variance (ANOVA) is used for analyzing the effect of process parameters in ISF. The result shows that the step-down size, feed rate of the tool, and wall angle are significant process parameter and their contributions for ISF are 85.86%, 1.12%, and 12.29%, respectively.


Metals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1003 ◽  
Author(s):  
Xiao Xiao ◽  
Jin-Jae Kim ◽  
Myoung-Pyo Hong ◽  
Sen Yang ◽  
Young-Suk Kim

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.


2015 ◽  
Author(s):  
Daniel de Castro Maciel ◽  
Gilmar Cordeiro da Silva ◽  
Luís Henrique Andrade Maia ◽  
Lúcio Flávio Santos Patrício ◽  
Jánes Landre Júnior

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