Optimum clearance determination in blanking coarse-grained non-oriented electrical steel sheets: experiment and simulation

He, Ji; Wang, Zhe; Li, Shuhui; Dong, Li Min; Cao, Xudong; Zhang, Weigang

doi:10.1007/s12289-018-1435-2

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Li et al [13] simulated the hot blanking process of boron steel, employing various fracture criteria and finding that Oyane and Brozzo criteria aligned well with experimental results. Liu et al [14] used FEM to reduce die-roll size in the fine blanking process by modifying the die structure, while He et al [15] optimized the clearance range in blanking processes through FEM, determining an optimal clearance for non-oriented electrical steel sheets. Sahli et al [16] observed that a reduction in clearance could delay fracture initiation, with FEM simulation results aligning with experimental data.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Application for Fine-Blanked Edge Quality in Rolled Steel Sheets

Nakkhlai

2023

GEOMATE

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The backing plate plays a crucial role in drum brakes, pushing the brake shoe against the drum for effective vehicle braking and road safety. Manufactured from steel sheets, it exhibits properties akin to a composite material, with characteristics varying based on directionality. The backing plate's profile features a continuous series of arcs, making the fine-blanked edge quality contingent on the arc radius. This research delved into fineblanked manufacturing through a combination of experimentation and simulation to discern the impact of the arc radius on edge quality. Employing a scanning electron microscope for edge quality investigation and benchmarking the finite element model against it, the study ensured a comprehensive understanding. The wellaligned finite element model served as input data for training an artificial neural network (ANN), specifically engineered to accurately estimate backing plate edge quality. This ANN is anticipated to be instrumental in designing future steel plate profiles boasting multiple arcs, offering precision in the manufacturing process for enhanced edge quality.

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Section: Introductionmentioning

confidence: 99%

Neural Network Application for Fine-Blanked Edge Quality in Rolled Steel Sheets

Nakkhlai

2023

GEOMATE

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“…Hambli [27] proposed the fast modelling the edge formation using anartificial neural network. Recent finite element simulations have led to the description of sheared edge morphology in terms of crack propagation [28]. Nevertheless, there is a lack in the literature concerning the effect of the cutting clearance on the magnetic properties of nonoriented electrical steels, particularly in relation to the mechanical characteristics of the deformation process and magnetization processes in the medium frequency range.…”

Section: Introductionmentioning

confidence: 99%

Correlation between Cutting Clearance, Deformation Texture, and Magnetic Loss Prediction in Non-Oriented Electrical Steels

et al. 2021

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Manufacturing the magnetic cores in electrical machines impacts the magnetic performance of the electrical steel by inducing stresses near the cutting edge. In this paper, energy loss behaviour in non-oriented electrical steels punched with different cutting clearances before and after annealing is investigated. An experimental shear cutting tool was employed to punch the ring-shaped parts from electrical steels in a finished state with four different values of cutting clearance corresponding to 1%, 3%, 5%, and 7% of the sheet thickness. The effect of cutting clearance on the magnetic losses is derived and analysed by the statistical theory of losses and associated loss separation concept including the analysis of movable magnetic objects. In this framework, this paper assesses the combined effect of cutting clearance, frequency, and heat treatment on the hysteresis loops and iron losses in non-oriented FeSi electrical steels. Measurements have been performed from quasi-static to 400 Hz at peak induction Bp = 1.0 T. Both states before and after heat treatment have been considered. The excess loss is observed as the most sensitive loss component to cutting clearance and its magneto–structural correlation is quantified.

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