Application of response surface methodology for optimization of pulsating blank holder parameters in deep drawing process of Al 1050 rectangular parts

Mostafapour, Amir; Akbari, Amir; Nakhaei, Mohammad

doi:10.1007/s00170-016-9781-z

Cited by 15 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was found that HT970 indicated better deep drawing performance than HT1070 Inconel material. Mostafapour et al 25 proposed a systematic methodology for developing a second order statistical model and showed the relationship between drawing depth and input variables such as frequency of blank holder, amplitude of blank holder movement, and velocity of punch movement. Preedawiphat et al 26 carried out significant factor analysis to explain how localized friction coefficient impacts the sheet thinning.…”

Section: Introductionmentioning

confidence: 99%

Predicting and improving the novel process parameters involved in deep drawing process of Inconel 718 sheet at room temperature using hybrid DNN-SSO approach

Ganesan

Sambasivam

Ramadass

2023

The Journal of Strain Analysis for Engineering Design

View full text Add to dashboard Cite

The deep drawing technique is an important metal forming processes, and it is rarely used in the Inconel 718 sheet. The main purpose of this study is to perform a deep drawing process using the Inconel 718 alloy. In this research article, the Inconel 718 alloy sheet of 1 mm thickness is drawn into sheet metal cups, and defects such as thinning, and earing are controlled using selected input parameters such as Blank Holding Force (BHF) Blank Diameter (BD), and Punch Nose Radius (Rpn). A Box Behnken design (BBD) is used to evaluate the effects of output parameters. The hybrid Deep Neural Network (DNN) is used to predict the experimental outcomes obtained from the deep drawing process. For deep drawing process blank holding force is favorable for both thinning and earing. The minimum thinning value obtained during experimentation is 0.033 mm. During experimentation less earing value is 2.47 mm. Hybrid Deep Neural Network based Sparrow Search Optimization (DNN-SSO) gives the prediction model, where the values are much closer to the experimented model than RSM and non-Hybrid DNN. The minimum thinning obtained in the prediction model such as RSM, SSO-DNN, and DNN are 0.030, 0.0304, and 0.023 mm. Likewise, the minimum earing obtained from the predictive model is 2.65, 2.49, and 2.51 mm respectively. The minimum error is found in the hybrid DNN and the average RMSE for thinning is 0.002 and earing is 0.0024. The regression coefficient of thinning and earing is 99% which proves the experimental outcomes matches with RSM validation.

show abstract

Section: Introductionmentioning

confidence: 99%