Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming

Trzepieciński, Tomasz; Kubit, Andrzej; Fejkiel, Romuald; Chodoła, Łukasz; Ficek, Daniel; Szczęsny, Ireneusz

doi:10.3390/ma14195887

Cited by 4 publications

(2 citation statements)

References 70 publications

(67 reference statements)

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“…It is worth mentioning that the scatter of stress increases after reaching maximum force. A similar effect (increased dispersion of test results after reaching maximum force) can be found in the paper [32] for DC04 low-carbon steel. Additionally, statistical analysis of the tensile test of S355 steel can be found in [33], where the standard deviation for 1089 specimens was 25.1 MPa and 25.4 MPa for tensile strength and yield strength, respectively.…”

Section: Resultssupporting

confidence: 83%

Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples

Strzelecki,

Sempruch

2024

Applied Sciences

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The paper presents guidelines included in the ISO 12107:2003 standard regarding the number of samples for determining the fatigue characteristics in the high cycle range. The proposed normative values were compared with the classical statistical approach. Fatigue test results for S355J2+C steel for rotary bending were used for verification. In addition, the error and power of a statistical test were determined for characteristics with different sample sizes. It determined the number of specimens to estimate the fatigue curve, which required significant error and power of the statistical test.

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Section: Resultssupporting

confidence: 83%

Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples

Strzelecki,

Sempruch

2024

Applied Sciences

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“…In 1998, Forcellese et al [ 34 ] evaluated the effect of the training set size of ANN on the reliability of the prediction of the springback in the free-bending process, which was also presented in the overview work by Pattanaik in 2013 [ 35 ]. In the following years, the ANN methods were further developed and applied to several forming technologies including deep drawing [ 36 ], ring rolling [ 37 ], electrohydraulic forming [ 38 ], bending [ 39 , 40 ], incremental forming [ 41 ], and several other application areas [ 42 , 43 , 44 , 45 , 46 ]. Hamouche et al [ 47 ] have developed a novel approach to select and classify a sheet metal process by machine-learning method from the final part geometry and achieved an accuracy of 89%.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming

et al. 2022

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Single point incremental forming (SPIF) is one of the most promising technologies for the manufacturing of sheet metal prototypes and parts in small quantities. Similar to other forming processes, the design of the SPIF process is a demanding task. Nowadays, the design process is usually performed using numerical simulations and virtual models. The modelling of the SPIF process faces several challenges, including extremely long computational times caused by long tool paths and the complexity of the problem. Path determination is also a demanding task. This paper presents a finite element (FE) analysis of an incrementally formed truncated pyramid compared to experimental validation. Focus was placed on a possible simplification of the FE process modelling and its impact on the reliability of the results obtained, especially on the geometric accuracy of the part and bottom pillowing effect. The FE modelling of SPIF process was performed with the software ABAQUS, while the experiment was performed on a conventional milling machine. Low-carbon steel DC04 was used. The results confirm that by implementing mass scaling and/or time scaling, the required calculation time can be significantly reduced without substantially affecting the pillowing accuracy. An innovative artificial neural network (ANN) approach was selected to find the optimal values of mesh size and mass scaling in term of minimal bottom pillowing error. However, care should be taken when increasing the element size, as it has a significant impact on the pillow effect at the bottom of the formed part. In the range of selected mass scaling and element size, the smallest geometrical error regarding the experimental part was obtained by mass scaling of 19.01 and tool velocity of 16.49 m/s at the mesh size of 1 × 1 mm. The obtained results enable significant reduction of the computational time and can be applied in the future for other incrementally formed shapes as well.

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The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Process

2022

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Lubrication is a basic and relatively effective way to reduce friction in sheet metal forming operations. The drive to eliminate synthetic and mineral oils, which are difficult to recycle, from the manufacturing process has opened up opportunities for the use of vegetable-based bio-lubricants. This article presents a comparison of the lubrication performance of two non-edible oils (karanja and moringa) with the most frequently tested edible oils (sunflower and rape-seed). Deep drawing quality low-carbon steel sheets DC04, commonly used in the automotive industry, were used as the test material. Friction tests were carried out under various lubricants and normal pressures in the range between 3 and 12 MPa using the strip drawing test. Furthermore, a study was also made of the effect of a change in the surface topography and the mechanical properties of the sheet metal due to plastic deformation resulting from friction. It was found that under the most favorable lubrication conditions (sample pre-strain 21%, nominal pressure 6 MPa), karanja oil reduced the coefficient of friction by approximately 33%. Both non-edible lubricants provided the best lubrication when testing samples pre-strained at 7% under the whole range of nominal pressures. It was also revealed that in the case of the smallest pre-straining of the specimens (7%), karanja oil was the most effective within nominal pressures of 3–6 MPa, while at higher pressures (9–12 MPa), the moringa oil lowered the value of the coefficient of friction to a greater extent.

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Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming

Cited by 4 publications

References 70 publications

Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples

Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples

Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming

The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Process

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