Simulation-based digital twin for the manufacturing of thermoplastic composites

Hürkamp, André; Lorenz, Ralph D.; Ossowski, Tim; Behrens, Bernd‐Arno; Dröder, Klaus

doi:10.1016/j.procir.2021.05.001

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…To accomplish this, process control must be added to the monitoring output. With enough data representing the function of the manufacturing machine/process, as well as the data gathered from an accurate process monitoring setup (in other words, a setup that captures approaching 100% of process variation, depending on the manufacturing tolerances of the component being produced), it is possible to develop an in-depth digital twin that can be simulated [19] and used for process control. This is especially possible when the digital twin employs machine learning along with simulation, as the simulation can improve training through techniques such as reinforcement or agent-based learning [20].…”

Section: Process Controlmentioning

confidence: 99%

Digital Thread Roadmap for Manufacturing and Health Monitoring the Life Cycle of Composite Aerospace Components

Eskue

2023

Aerospace

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This paper provides a detailed review of a digital thread for composite aerospace components. The current state of the digital thread continues to progress and at an ever-accelerating rate due to advancements in supporting technologies such as AI, data capture/processing/storage, sensors, simulation, and blockchain. While the individual steps that make up the digital thread have made manufacturing innovation and benefits possible, the connection points of the thread are not consistently solid, with many experiments and proof-of-concepts being conducted, but with few full digital threads in deployment. Key gaps include the ability to handle such large and continuous amounts of data, the infrastructure needed to capture and process them for insight, and the AI-based analytics to build and scale enough to obtain the expected exponential benefits for life cycle insight and manufacturing optimization. Though some of these gaps may take specific technology innovations to advance, there is a specific roadmap that can be deployed immediately in order to obtain “rolling ROI” benefits that will scale in value as this cycle is repeated across the product line.

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Section: Process Controlmentioning

confidence: 99%

Digital Thread Roadmap for Manufacturing and Health Monitoring the Life Cycle of Composite Aerospace Components

Eskue

2023

Aerospace

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“…In the forming simulation, the temperature is chosen marginally above the matrix melting temperature, which is around 220°C for polyamide 6. Punch and die are modelled as rigid bodies, while the organo sheet is described by the material card MAT_249_REINFORCED_THERMOPLASTIC [10]. Within this material card, the fibers are described as an anisotropic-hyperelastic material and the matrix as elasto-plastic.…”

Section: Numerical Modelmentioning

confidence: 99%

Surrogate model of the thermoforming of fiber reinforced thermoplastics

MIDDELHOFF

2023

Materials Research Proceedings

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Abstract. In the thermoforming process shearing of fiber reinforced thermoplastics (FRTP) leads to wrinkle formation and is therefore one of the most critical deformation mechanism. The target value to predict wrinkle formation is the shear angle which is considered in this paper. To predict the shear angle distribution during the thermoforming of FRTPs a Convolutional Neural Network (CNN) is used. The approach is based on the consideration of the principal curvature characteristics, resulting in the reduction of any complex geometry to planar, parabolic, elliptical, semi-spherical and hyperbolic areas. In similar research, CNNs are trained for each specific geometry. In this work, the CNN refers to a database of the mentioned curvature characteristics. In theory, all forming geometries can be created from these, which is why the CNN is enabled to predict the forming result of any complex geometry. In this paper, the benchmark geometry of a double dome is selected as the validation geometry and the error in the prediction of the shear angle is compared with the results of numerical forming simulations performed.

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“…Hürkamp et al 19,25 generated training data for machine learning methods using FEM and developed an surrogate model capable of predicting interface bonding strength quality based on process settings. Hürkamp et al 26 introduced a digital twin framework that combines simulation, Proper Orthogonal Decomposition (POD), and machine learning to predict the temperature field in the overmolding process. However, the results obtained by Hürkamp 19 lacks an assessment of the significance of process parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites

Yin,

Zhai,

Ding

2024

Polymer Composites

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The focus of this study is to apply finite element method (FEM) and machine learning methods to investigate the interfacial bonding strength of continuous fiber reinforced thermoset composite (TSC)‐thermoplastic structures manufactured through co‐curing and overmolding processes, with polyamide 6 (PA 6) as the thermoplastic material. A model for interfacial healing degree in TSC‐PA 6 structures was developed. Then, FEM was employed to study the influence of various injection overmolding process parameters on the interfacial bonding strength of TSC‐PA 6 structures. The results show that there is a strong correlation between the degree of interface healing and the bonding strength. Subsequently, six machine learning methods were employed to correlate interfacial healing degree with diverse injection molding process parameters. Simulation data were utilized for training, calibration, and validation of the six machine learning models. Based on the results of simulation and machine learning predictions, a quantitative analysis of the significance of injection molding process parameters on healing degree was conducted. These parameters are ranked in descending order of importance as follows: insert temperature, melt temperature, and injection rate.Highlights The interfacial healing degree model of PA 6 was established and validated through the integration FEM and experiment. Six machine learning models were built to predict the interfacial healing degree. Grad boosting performed best in predicting the interfacial healing degree. Insert temperature had the greatest impact on the interface healing degree.

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Simulation-based digital twin for the manufacturing of thermoplastic composites

Cited by 15 publications

References 7 publications

Digital Thread Roadmap for Manufacturing and Health Monitoring the Life Cycle of Composite Aerospace Components

Digital Thread Roadmap for Manufacturing and Health Monitoring the Life Cycle of Composite Aerospace Components

Surrogate model of the thermoforming of fiber reinforced thermoplastics

Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites

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