Influence of process parameters on residual stresses in deep-drawing process with FEM and experimental evaluations

Kardan, Masoud; Parvizi, Ali; Askari, Ali

doi:10.1007/s40430-018-1085-9

Cited by 22 publications

(10 citation statements)

References 17 publications

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“…In this study, the parameters considered include the initial blank thickness, punch and die shoulder radii, blank-holder force, punch velocity, and coefficients of friction between die-blank, holder-blank, and punch-blank. Through the application of analysis of variance, the most important parameters can be determined, and the optimal value is obtained to minimize residual stress in the deep-drawing process [8]. Experiments on the effect of ironing on the resi dual stress and its distribution in the deep drawing cups were carried out.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Determination of the effect of thickness reduction ratio, die angle, and coefficient of friction on residual stresses in ironing process: an analysis using computer simulation

Faizin¹,

Londen²,

Pramono³

et al. 2021

EEJET

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Ironing is a part of the group of metal forming processes, the process of reducing the thickness of the wall of a cup-shaped product. External load required to process metal forming, can cause residual stress. Residual stress can be beneficial or detrimental depending on the function of the product, the magnitude, and direction of the residual stress. Residual stress can act as an additional load on a given load. Residual stress can affect product quality, namely: dimensional accuracy, surface roughness, and mechanical properties. The speed of the ironing process is strongly influenced by the mechanical properties of the cup material, Thickness Reduction Ratio (TRR), and press tool design. The ironing process has a limited TRR value and if it is exceeded it results in product damage. Various studies on ironing were carried out to obtain an optimal process. In this research, stress analysis was carried out using the ANSYS software modeling simulation to obtain the occurring residual stress during the ironing process. The analysis was carried out by varying the TRR from 20 % to 30 %, the die angle from 25° to 30°, and the coefficient of friction from 0.05 to 0.15. Furthermore, processing and analysis of the stress analysis data are carried out to obtain the most dominant variables affecting the residual stress and the variable value that produces the lowest residual stress. Stress analysis was carried out on AA1100 aluminum cups with an outer diameter of 37 mm, a height of 20 mm, and a wall thickness of 2 mm. The results show that TRR and coefficient of friction are the most dominant variables affecting residual stress, while die angle has no significant effect. The lowest residual stress occurs at TRR 30 % and coefficient of friction 0.15

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Determination of the effect of thickness reduction ratio, die angle, and coefficient of friction on residual stresses in ironing process: an analysis using computer simulation

Faizin¹,

Londen²,

Pramono³

et al. 2021

EEJET

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“…AISI 1060 steel sheets were used for deep drawing studies using the Taguchi method [12]. Eight process variables, including blank thickness, die radius, punch radius, lubrication at the blank-die, blank-blank holder, and blank punch interfaces, blank holder force, and punch speed, were evaluated for their effects on punch force and thickness distribution and ABAQUS modeling software, thickness distribution is studied.…”

Section: Introductionmentioning

confidence: 99%

Determination of optimum process parameters for minimum punch force in deep drawing using numerical simulation

Sisil Amose,

Elsie Femina,

Sivarajan

2023

J. Phys.: Conf. Ser.

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This article represents the most common sheet formation method, deep drawing. This technology is employed in the production of the automotive and aerospace industries. There are various aspects that have an impact on the deep drawing process; these factors are known as the deep drawing process parameters. Finite element analysis and Taguchi Techniques are coupled to find out the optimum deep drawing parameters. Six process parameters such as die corner radius, punch corner radius, punch speed, friction between blank and die, friction between blank and blank holder and friction between blank and punch are used for analysis. Punch force is considered as response variable. The Taguchi method is employed to determine the optimal process parameters for the deep drawing process. Based on SN ratio analysis, optimum deep drawing parameters for punch force were found to be die corner radius of 7mm, punch corner radius of 7mm, punch speed of 0.2mm/s, friction between blank and die of 0.08, friction between blank and blank holder of 0.08 and friction between blank and punch of 0.10. From ANOVO, it was found that die corner radius was the most significant process parameter influencing minimum punch force.

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“…The good performances of the deep-drawing process depend on the correct setting of the process parameters that govern the phenomenon such as pressure on the blank holder, gap between die and punch, radius of the tools, lubrication and initial geometry of the blank. In the literature, in fact, several studies evaluate the effects of different process parameters on the quality of the final product [4,5].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization and Kriging Metamodeling of Deep-Drawing Process to Obtain a Regulation Curve of Blank Holder Force

Palmieri

Lorusso

Tricarico

2021

Metals

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In recent decades, the automotive industry has had a constant evolution with consequent enhancement of products quality. In industrial applications, quality may be defined as conformance to product specifications and repeatability of manufacturing process. Moreover, in the modern era of Industry 4.0, research on technological innovation has made the real-time control of manufacturing process possible. Moving from the above context, a method is proposed to perform real-time control of a deep-drawing process, using the stamping of the upper front cross member of a car chassis as industrial case study. In particular, it is proposed to calibrate the force acting on the blank holder, defining a regulation curve that considers the material yield stress and the friction coefficient as the main noise variables of the process. Firstly, deep-drawing process was modeled by using commercial Finite Element (FE) software AutoForm. By means of AutoForm Sigma tool, the stability and capability of deep-drawing process were analyzed. Numerical results were then exploited to create metamodels, by using the kriging technique, which shows the relationships between the process parameters and appropriate quality indices. Multi-objective optimization with a desirability function was carried out to identify the optimal values of input parameters for deep-drawing process. Finally, the desired regulation curve was obtained by maximizing total desirability. The resulting regulation curve can be exploited as a useful tool for real-time control of the force acting on the blank holder.

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Influence of process parameters on residual stresses in deep-drawing process with FEM and experimental evaluations

Cited by 22 publications

References 17 publications

Determination of the effect of thickness reduction ratio, die angle, and coefficient of friction on residual stresses in ironing process: an analysis using computer simulation

Determination of the effect of thickness reduction ratio, die angle, and coefficient of friction on residual stresses in ironing process: an analysis using computer simulation

Determination of optimum process parameters for minimum punch force in deep drawing using numerical simulation

Robust Optimization and Kriging Metamodeling of Deep-Drawing Process to Obtain a Regulation Curve of Blank Holder Force

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