A System Identification and Implementation of a Soft Sensor for Freeform Bending

Stebner, Sophie Charlotte; Maier, Daniel; Ismail, Alaa M.; Balyan, Shubham; Dölz, Michael; Lohmann, Boris; Volk, Wolfram; Münstermann, Sebastian

doi:10.3390/ma14164549

Cited by 10 publications

(10 citation statements)

References 26 publications

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“…Furthermore, sample ten in the tensile tests of the base material poses an outlier. This is due to a strong scatter in the investigated material [4]. Fig.…”

Section: Resultsmentioning

confidence: 96%

“…All simulations were done using J2 plasticity theory, as is default in Abaqus solver. As the Abaqus solver does not have a predefined output variable for hardness and the strength cannot be taken directly from the bent tube, the conversion was done according to the findings of the authors in [4]. They proposed that hardness up to necking can be converted into strength in multiplying the hardness by 2.88, see eq.…”

Section: Modelmentioning

confidence: 99%

“…Due to the bending process, the mechanical properties of the steel tubes are influencednamely due to strain hardening and a change in residual stress state. Yet, both of these mechanical characteristics not only influence further manufacturing steps but also determine the service application of the tube [4,5]. Thus, inline monitoring of the mechanical properties is essential, as this will allow the implementation of a closedloop property control.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [4] introduce a soft sensor based on an Extended Kalman Filter for freeform bending of circular steel tubes that can infer residual hoop stresses, local strength and plasticity level based on Ultrasonic Contact Impedance (UCI-) hardness measurements. UCI-hardness measurements are taken on the steel tubes and saved within a .csv file.…”

Section: Introductionmentioning

confidence: 99%

“…The prediction of the plasticity level is based on the prediction of the local strength utilizing tensile test data. For further information on the soft sensor, the authors refer to [4]. This soft sensor now enables component evaluation already during the forming process.…”

Section: Introductionmentioning

confidence: 99%

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Extension of a Simulation Model of the Freeform Bending Process as Part of a Soft Sensor for a Property Control

Stebner

Maier

Ismail

et al. 2022

KEM

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This work introduces an Abaqus CAE simulation model and its validation for freeform bending with movable die, which is extended in the simulation of a weld seam for the longitudinally welded tube. The superordinate goal is the development of a soft sensor, that can derive mechanical properties of a freeform bent tube as a basis for a closed-loop control. To guarantee a close monitoring of the mechanical properties, the soft sensor needs to be able to extrapolate the mechanical properties spatially. Because the investigated steel tubes are longitudinally welded, the weld depicts a disturbance regarding the rotational symmetry. In developing and validating a numerical simulation of the process, that quantitatively describes the influences the weld has on the mechanical properties, a significant improvement of the qualitative and quantitative prediction of the soft sensor can be achieved. The numerical modelling is done based on tensile tests on material taken from the weld seam, where hardness measurements are then used for the local validation of the model. The validated model now provides a time as well as cost efficient way of a primary investigation of the mechanical properties, especially regarding the local strength of the steel tube for a soft sensor and as input data for a feed forward control in the machine. Therefore, this work represents an important addition to the superordinate goal of developing a closed-loop property-control based on a soft sensor for freeform bending with movable die.

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“…Furthermore, sample ten in the tensile tests of the base material poses an outlier. This is due to a strong scatter in the investigated material [4]. Fig.…”

Section: Resultsmentioning

confidence: 96%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Extension of a Simulation Model of the Freeform Bending Process as Part of a Soft Sensor for a Property Control

Stebner

Maier

Ismail

et al. 2022

KEM

Self Cite

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show abstract

Forming-based geometric correction methods for thin-walled metallic components: a selective review

Zhou

et al. 2023

Int J Adv Manuf Technol

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Geometric correction processes contribute to zero-defect manufacturing for improved product quality. Thin-walled metallic components are widely used in numerous applications such as electric vehicles and aircraft due to the lightweight feature, facilitating to achieve zero-emission goals. However, many components suffer geometric imperfections and inaccuracies such as undesired curvatures and twists, seriously affecting subsequent manufacturing operations, for example, automatic welding and assembly. Geometric correction techniques have been established to address these issues, but they have drawn little attention in the scientific community despite their wide applications and urgent demands in the industry. Due to the strict geometric tolerances demanded in high-volume automated production, it is urgent to increase the knowledge needed to develop new techniques to address future industrial challenges. This review paper presents an overview of typical geometric defects in thin-walled components and clarifies the associated underlying generation mechanisms. Attempts have also been made to discuss and categorize geometric correction techniques based on different forming mechanisms. The challenges in correcting complex thin-walled products are discussed. This review paper also provides researchers and engineers with directions to find and select appropriate geometric correction methods to achieve high geometric accuracy for thin-walled metallic components.

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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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A System Identification and Implementation of a Soft Sensor for Freeform Bending

Cited by 10 publications

References 26 publications

Extension of a Simulation Model of the Freeform Bending Process as Part of a Soft Sensor for a Property Control

Extension of a Simulation Model of the Freeform Bending Process as Part of a Soft Sensor for a Property Control

Forming-based geometric correction methods for thin-walled metallic components: a selective review

A physically-informed machine learning model for freeform bending

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