Machine Learning (ML)-Based Prediction and Compensation of Springback for Tube Bending

Ma, Jun; Li, Hengyun; Chen, G. Y.; Welo, Torgeir; Li, G. J.

doi:10.1007/978-3-030-75381-8_13

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…To summarize, rebound has always been a key factor inhibiting bending quality, increasing mold and product costs, and reducing manufacturing efficiency. Therefore, rebound prediction has received increasing attention and research [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Inamdar et al [13] utilized backpropagation neural networks to predict and control the rebound of V-shaped tubes. Ma et al [14] employed machine learning (ML) to predict and compensate for tube-bending. The above kinds of literature are based on data training, using different ML methods to predict the rebound, and have achieved good results under sufficient samples.…”

Section: Introductionmentioning

confidence: 99%

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A GAN–SVR Prediction Method of the Metal Tube‐Bending Rebound with Small Samples

Zhang,

Fang,

et al. 2023

Journal of Sensors

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This paper investigates a predictive algorithm for the angle of the metal tube-bending rebound with small samples. First, the generative adversarial network (GAN) approach is introduced to address the issues of insufficient sample data. The proposed method can realize data augmentation through a generator, enhancing training effectiveness compared to conventional model-based and experimental prediction methods. To further reduce the problems caused by the small samples, the Wasserstein distance is utilized as the optimization objective for the GAN approach. Second, after obtaining the augmented dataset, Support Vector Regression (SVR) is employed to predict the rebound model of the metal tube-bending. A novel predictive algorithm for the angle of the metal tube-bending rebound based on GAN–SVR is proposed. It exhibits that the GAN–SVR owns more positive prediction ability and error when dealing with small samples than conventional GAN-radial basis function methd (GAN–BP) and GAN–convolutional neural networks. Finally, the effectiveness of the proposed method is validated through experimental results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A GAN–SVR Prediction Method of the Metal Tube‐Bending Rebound with Small Samples

Zhang,

Fang,

et al. 2023

Journal of Sensors

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A physically-informed machine learning model for freeform bending

Lechner,

Scandola,

Maier

et al. 2024

J Intell Manuf

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This work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die by computing an elastic representation of the tube’s deformation with beam theory at each time step. This elastic representation serves as input for a regression model similar to a partially connected neural network. This physically-informed machine learning model generalizes the constant training radii to complex bend geometries consisting of transitional sections and true spline geometries. It is compared to a benchmark finite element simulation and has an improved prediction quality for complex kinematics while reducing the computation time by four orders of magnitude.

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Model-based closed-loop process control for the manufacturing of hairpin coils

Wirth,

Hausmann,

Fleischer

2024

Prod. Eng. Res. Devel.

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As a consequence of the increasing market share of hybrid and all-electric mobility solutions, there is a need for robust manufacturing technologies that enable the efficient production of powerful electric traction motors in automotive industry. In the context of high-quality distributed stator windings, the so-called hairpin technology already meets the automotive demand regarding productivity – but process reliability is still limited by variations in the mechanical and geometric properties of the enameled rectangular copper wire leading to different springback effects within the forming processes. Against this background, a model-based closed-loop process control for the sequential tool-bound bending of hairpin coils is introduced and validated in this paper.

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Machine Learning (ML)-Based Prediction and Compensation of Springback for Tube Bending

Cited by 9 publications

References 11 publications

A GAN–SVR Prediction Method of the Metal Tube‐Bending Rebound with Small Samples

A GAN–SVR Prediction Method of the Metal Tube‐Bending Rebound with Small Samples

A physically-informed machine learning model for freeform bending

Model-based closed-loop process control for the manufacturing of hairpin coils

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