State-of-the-art and future challenge in fine-blanking technology

Zheng, Qide; Zhuang, Xincun; Zhao, Zhen

doi:10.1007/s11740-018-0839-7

Cited by 31 publications

(12 citation statements)

References 47 publications

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“…Other authors predict that artificial intelligence and machine learning will have enormous effects on blanking processes [5], while the first applications have already been presented. For example, neural networks can be used to approximate the effects of fluctuating process parameters on geometric product properties, whereby training data can be obtained through FEM simulations, as presented by Stanke et al [6] or Hambli and Guerin [7].…”

Section: Introductionmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.

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Section: Introductionmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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“…The use of a negative clearance enhances the quality of the fine blanking without creation of cracks in the specimen [1]. This optimization is also highlighted by Zheng as a challenge in fine blanking technology [2]. It is characterized by a die diameter finer than punch diameter, so that the punch must be stopped before crossing the thickness of the raw material, else both punch and die would be damaged.…”

Section: Introductionmentioning

confidence: 99%

Influence of mechanical properties and alloying elements of low carbon steels on fine blanking and heat treatment processes response

Chiavazza

Marnier

Achille

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The effect of the negative clearances between punch and die of a flange with different fine blanking half-cutting depths was investigated for two low carbon steels with various elongation rates, C18E (0.18%C) and 20MnB5 steels (0.2%C, with upper mechanical properties than the carbon steel), industrially used for forming car seat parts. A specific tool adaptable on a tensile machine was built to determine the force needed to fine blank a simple geometry, in the objective to further transpose the obtained results to the complex geometry of industrial parts. Heat treatment was carried out to optimize the microstructure, hardness and dimensional modifications for the combination material/clearance/half-cutting height tested. Increasing the elongation rate of the raw material allows to reach a better formability and reduces the force needed to create a given geometry, but cracks were detected for the extreme deformation rate. Moreover, after fine blanking and heat treatment operations, different microstructures were observed according to the material grade, stable for low alloy 20MnB5 steels (martensitic microstructure) while non reproducible for the low carbon steel (mix of ferrite, bainite and martensite).

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“…Liu et al [5] proposed an optimization analysis which consists of minimizing die roll dimensions, an indenter mechanism set on cutting edge, and finite element analysis to validate the optimization; the study shows the optimized fine-blanking die can limit blanking deformation and the increase of die roll. Zheng et al [6] discussed the effects of die roll dimension for evaluating product quality due to changes to product shape and material, the evolution of blank holder structure, and blanking press, which acts as a crucial part of fine-blanking process quality control in electric automotive industries. Karbaukh et al [7] proposed the multiple different wedge-shaped knives for rolled-stock cutting equipment and process, which converted deformation energy from machine frame and drive into hydraulic press, namely, a stress concentrator for effective cutting work.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation and optimization of fine-blanking process for copper alloy sheet

Kuo

Liu

Li³

et al. 2021

Int J Adv Manuf Technol

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When the fine-blanking process is used, secondary grinding or processing can be omitted because the shear surface of fine-blanking parts can achieve almost zero fracture zone requirements. The primary objective of the fine-blanking process is to reduce the fracture zone depth and die roll zone width. This study used a 2.5-mm-thick central processing unit (CPU) thermal heat spreader as an example. Finite element analysis software was employed to simulate and optimize the main eight process parameters that affect the fracture zone depth and die roll zone width after fine-blanking: the V-ring shape angle, V-ring height of the blank holder, V-ring height of the cavity, V-ring position, blank holder force, counter punch force, die clearance, and blanking velocity. Simulation analysis was conducted using the L18 (21 × 37) Taguchi orthogonal array experimental combination. The simulation results of the fracture zone depth and die roll zone width were optimized and analyzed as quality objectives using Taguchi’s smaller-the-better design. The analysis results revealed that with fracture zone depth as the quality objective, 0.164 mm was the optimal value, and counter punch force made the largest contribution of 25.89%. In addition, with die roll zone width as the quality objective, the optimal value was 1.274 mm, and V-ring height of the cavity made the largest contribution of 29.45%. Subsequently, this study selected fracture zone depth and die roll zone width as multicriteria quality objectives and used the robust multicriteria optimal approach and Pareto-optimal solutions to perform multicriteria optimization analysis. The results met the industry’s fraction zone depth standard (below 12% of blank thickness) and achieved a smaller die roll zone width.

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State-of-the-art and future challenge in fine-blanking technology

Cited by 31 publications

References 47 publications

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Influence of mechanical properties and alloying elements of low carbon steels on fine blanking and heat treatment processes response

Numerical simulation and optimization of fine-blanking process for copper alloy sheet

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