Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawing

Ghennai, Walid; Boussaid, Ouzine; Bendjama, Hocine; Haddag, Badis; Nouari, Mohammed

doi:10.1007/s00170-018-2700-8

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…Consequently, only the four coefficients: F, G, H and N can be retained. These factors can be determined empirically from a monotonic tensile test following various sampling directions Eq (5) [35].…”

Section: Modelling and Numerical Simulationmentioning

confidence: 99%

Experimental investigation and numerical optimization of sheet metal forming limits during deep drawing process of DD14 steel

Hamza,

Boussaid,

Hamadache

et al. 2024

Res. Eng. Struct. Mat.

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This work aims to experimentally study the deep drawability of DD14 hot-rolled steel sheets and to optimize numerically the material formability. The material anisotropy was confirmed by metallographic analysis of the material and tensile tests on specimens taken in three orientations with respect to the sheet rolling direction. Where the stress-strain curves show a sensitivity of material to the Piobert-Lüders phenomenon. The die pressure and the blank holder were considered as controlling factors on plastic instability and the success of the deep drawing operation. The forming limit curves (FLC) were plotted to highlight the different deformation modes that the sheet metal underwent during the deep drawing process. The collected deep-drawn parts observation shows severe plastic instability, until fracture. The initial plastic anisotropy is determined by the Hill48 quadratic criterion and the work hardening by the Hollomon power law. It results, during the experimental tests, that the front bottom corners of the deep drawn part are the areas at high risk of damage. The various numerical simulation scenarios followed by comparisons with the experimental results, allowed us to confirm the optimum conditions that minimize the material's plastic instability risks, forming operation “Step” and BHP “Amplitude”.

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Section: Modelling and Numerical Simulationmentioning

confidence: 99%

Experimental investigation and numerical optimization of sheet metal forming limits during deep drawing process of DD14 steel

Hamza,

Boussaid,

Hamadache

et al. 2024

Res. Eng. Struct. Mat.

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“…This process has seen extensive development owing to its widespread application in industry. In recent years, to meet the needs of manufacturers in terms of quality and competitiveness, many investigations have been devoted to the numerical simulation of this process in order to optimize and ensure product feasibility [1][2][3]. At a local company, EIMS-Miliana-Algeria [4], extra-deep drawing is intensively used to manufacture products with relatively complex shapes.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of an extra-deep drawing process with industrial parameters: formability analysis and process optimization

Belguebli,

Zidane,

Hadj Amar

et al. 2024

Frattura Ed Integrità Strutturale

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Extra-deep drawing is one of the most important sheet metal forming processes. The appearance of rupture and wrinkling are the most commonly encountered problems in this process. These defects are also very common at a local company, particularly in the manufacturing of wheelbarrow trays. As a consequence, substantial time and costs are incurred in industrial production. To thoroughly analyze and address these defects, the extra-deep drawing operation of the wheelbarrow tray was modeled by the FE method using Abaqus software. A defect-free manufactured wheelbarrow tray was used to validate the accuracy of the numerical approach. Precise measurements of its dimensions were taken through the reverse engineering process using a 3D scanner. Furthermore, other measurements were made with an ultrasonic thickness gauge to have more precise measurements of the product’s thickness. Comparing experimental and numerical results showed good agreement. The outcomes of the numerical analysis indicate that the final shape of the wheelbarrow tray does not contain rupture or wrinkling defects, accurately corresponding to the real cases manufactured at the company. Numerical modeling and optimization of the extra-deep drawing process performed in this study could potentially reduce production losses and improve the overall efficiency of industrial manufacturing.

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“…However, additive manufacturing can produce parts with weaker mechanical qualities, including electrical and thermal conductivity, optical clarity, and strength. This study used the design of experiment (DOE) method and input parameter adjustments to improve the mechanical properties of FDM components [23][24][25][26]. Examining the mechanical properties of samples through various tests, including tensile, roughness, and bending tests, is crucial for optimizing the 3D printing process in polymers.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modelling of Fused Deposition Modeling (FDM) 3D Printing of Poly Vinyl Alcohol Parts through Statistical Design of Experiments Approach

Moradi,

Karamimoghadam,

Meiabadi

et al. 2023

Mathematics

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This paper explores the 3D printing of poly vinyl alcohol (PVA) using the fused deposition modeling (FDM) process by conducting statistical modeling and optimization. This study focuses on varying the infill percentage (10–50%) and patterns (Cubic, Gyroid, tri-hexagon and triangle, Grid) as input parameters for the response surface methodology (DOE) while measuring modulus, elongation at break, and weight as experimental responses. To determine the optimal parameters, a regression equation analysis was conducted to identify the most significant parameters. The results indicate that both input parameters significantly impact the output responses. The Design Expert software was utilized to create surface and residual plots, and the interaction between the two input parameters shows that increasing the infill percentage (IP) leads to printing heavier samples, while the patterns do not affect the weight of the parts due to close printing structures. On the contrary, the discrepancy between the predicted and actual responses for the optimal samples is below 15%. This level of error is deemed acceptable for the DOE experiments.

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Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawing

Cited by 14 publications

References 34 publications

Experimental investigation and numerical optimization of sheet metal forming limits during deep drawing process of DD14 steel

Experimental investigation and numerical optimization of sheet metal forming limits during deep drawing process of DD14 steel

Numerical investigation of an extra-deep drawing process with industrial parameters: formability analysis and process optimization

Mathematical Modelling of Fused Deposition Modeling (FDM) 3D Printing of Poly Vinyl Alcohol Parts through Statistical Design of Experiments Approach

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