Experimental and Numerical Simulation Investigation on Deep Drawing Process of Inconel 718 with and without Intermediate Annealing Thermal Treatments

Ulibarri, Unai; Galdos, Lander; Argandoña, Eneko Sáenz de; Mendiguren, Joseba

doi:10.3390/app10020581

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…It has high corrosion strength and antioxidant resistance at high temperatures due to the contents of molybdenum and chromium. 2 Besides it is a hard material due to the build-up edges and it has low thermal conductivity. In high temperatures it tends to a chemical reaction, and in microstructural view, there is an abrasive carbide presents in it.…”

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

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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.

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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

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“…[18][19][20] The optimal parameter is by the combination of surrogate optimisation and SVM. 21 the study analysed the geometrical attributes through a learning process by a neural network. The objective for optimising the geometric attributes is designed with the help of sheet thickness and uniformity.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

Sivam

Rajendran

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Deep drawing is a method of producing sheet metal. It is extensively used in the automobile, packaging and home appliance industries. Incorrect parameter selection might induce defects in stamped components. Trial and error can improve product accuracy, but at the expense of execution and calculation. An optimisation approach and a response surface model are used to determine the optimal parametric parameter design. This study proposes a full and efficient optimisation approach, starting with modelling by RSM (Response Surface Methodology) and concluding with optimal process parameter identification. Based on this, a neural network was developed to improve cylindrical cup deep drawing. Three characteristics of fabricating the cups are clearance, punch radius and coefficient of friction. This parameter demonstrates the resultant tool force, spring back, maximum forming limiting curve and maximum thinning rate of cylinder cups. Initially, the three inputs are varied to measure the outcome using simulation software by Altair inspire form. Then, the equations for each output are designed using quadratic-based linear model fitting. The generated objective function is then used to establish the ideal process parameters for the cylindrical cups. The proposed method is compared to real-time physical experiments. The optimised parameter microstructure at the high-stress zone and the product quality are satisfactory without defects.

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“…The thermal treatments for deep drawing materials are commonly employed in an effort to improve formability or reduce residual stresses. Unai Ulibarri et al [ 12 ] studied on the deep drawing process of Inconel 718 material with and without intermediate annealing thermal treatments based on the experimental and numerical simulation investigation. The behavior of the material in its as-received state and post-stretching thermal treatment state was analyzed and modeled.…”

Section: Introductionmentioning

confidence: 99%

Effect of Tool Geometry Parameters on the Formability of a Camera Cover in the Deep Drawing Process

Trung

Minh

2021

Materials

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The formability of the drawn part in the deep drawing process depends not only on the material properties, but also on the equipment used, metal flow control and tool parameters. The most common defects can be the thickening, stretching and splitting. However, the optimization of tools including the die and punch parameters leads to a reduction of the defects and improves the quality of the products. In this paper, the formability of the camera cover by aluminum alloy A1050 in the deep drawing process was examined relating to the tool geometry parameters based on numerical and experimental analyses. The results showed that the thickness was the smallest and the stress was the highest at one of the bottom corners where the biaxial stretching was the predominant mode of deformation. The problems of the thickening at the flange area, the stretching at the side wall and the splitting at the bottom corners could be prevented when the tool parameters were optimized that related to the thickness and stress. It was clear that the optimal thickness distribution of the camera cover was obtained by the design of tools with the best values—with the die edge radius 10 times, the pocket radius on the bottom of the die 5 times, and the punch nose radius 2.5 times the sheet thickness. Additionally, the quality of the camera cover was improved with a maximum thinning of 25% experimentally, and it was within the suggested maximum allowable thickness reduction of 45% for various industrial applications after optimizing the tool geometry parameters in the deep drawing process.

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Experimental and Numerical Simulation Investigation on Deep Drawing Process of Inconel 718 with and without Intermediate Annealing Thermal Treatments

Cited by 5 publications

References 27 publications

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

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

Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

Effect of Tool Geometry Parameters on the Formability of a Camera Cover in the Deep Drawing Process

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