Veröffentlichungen

@article{nokey,

title = {Adhesion-cohesion balance of prepreg tack in thermoset automated fiber placement. Part 2: Ply-ply cohesion through contact formation and autohesion},

author = {Dennis Budelmann and Carsten Schmidt and Dieter Meiners and Leif Steuernagel},

editor = {Composites Parts C},

url = {https://doi.org/10.1016/j.jcomc.2023.100396},

isbn = {2666-6820},

year  = {2023},

date = {2023-09-06},

journal = {Composites Part C},

issue = {12},

pages = {100396},

abstract = {Contact formation and autohesion with respect to their role as the major mechanisms governing the tack between thermoset prepregs in automated fiber placement were explored. Therefore, a novel 90° peel test with strictly separated and individually controllable compaction and debonding phases was employed for experimental tack characterization in a rheometer. Variation of compaction pressure, dwell time and temperature enabled the experimental isolation of contact formation and autohesion influences. The experimentally determined tack, ply-ply contact area and resin viscoelastic characteristics were used to parametrize simplified semi-empirical bond strength sub-models that have originally been developed for thermoplastic composite manufacturing techniques. The model prediction was validated successfully within the experimentally reproducible parameter range. Eventually, manufacturing scenarios for thermoset automated fiber placement (AFP) respecting different lay-up velocities (up to 1 m s−1), compaction pressures (up to 10 N mm−2) and both lay-up and mold temperatures (20–60 °C) were assessed in terms of estimated prepreg tack. The implication of both mechanisms, contact formation and autohesion, in the evolution of prepreg tackiness was found to be able to replicate the bell-shaped tack curves proposed by the adhesion-cohesion balance.},

keywords = {Automated Fiber Placement, Carbon Fibre, cohesion, Epoxy resin, interface, Prepreg},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tack of epoxy resin films for aerospace-grade prepregs: Influence of resin formulation, B-staging and toughening},

author = {Dennis Budelmann and Carsten Schmidt and Dieter Meiners},

url = {https://www.sciencedirect.com/science/article/pii/S0142941822002318},

doi = {https://doi.org/10.1016/j.polymertesting.2022.107709},

year  = {2022},

date = {2022-07-19},

urldate = {2022-07-19},

journal = {Polymer Testing},

volume = {114},

issue = {Volume 114, 2022},

abstract = {Aerospace-grade prepreg resin films based on multifunctional tetraglycidyl-4,4′-methylenedianiline (TGMDA), triglycidyl p-aminophenol (TGAP), Bisphenol A diglycidyl ether (DGEBA) and curing agent 4,4'diaminodiphenyl sulfone (DDS) are investigated in terms of tackiness by probe testing. The model epoxy systems are modified regarding the thermoplastic toughener content (polyethersulfone, PES) and the B-stage level, which is adjusted by cure prediction based on a model-free isoconversional method (Flynn-Wall-Ozawa). Additional DSC and rheological analysis are performed to study the thermal and viscoelastic material behavior in conjunction to its impact on temperature-dependent tack. Maximum achievable tack is found to decrease as a function of both degree of conversion and toughener content. Meanwhile, both influencing factors shift the tack maximum towards higher temperatures corresponding to increased flow characteristics attributed to evolving network formation and the incorporation of high molecular weight PES. In terms of absolute tack level and corresponding temperature, probe tack values similar to commercial prepreg systems (∼100 μJ mm−2) are recorded for TGMDA-based formulations containing 10 wt% PES at 20% pre-cure. Model formulations, which have neither been exposed to B-staging nor toughened, show exceptionally high tack below room temperature for all investigated epoxy prepolymers and are therefore not considered processable by automated fiber placement.},

keywords = {Automated Fiber Placement, Prepreg, Tack},

pubstate = {published},

tppubtype = {article}

}

@article{Denkena2021c,

title = {An optical-flow-based monitoring method for measuring translational motion in infrared-thermographic images of AFP processes},

author = {Berend Denkena and Carsten Schmidt and Marc Timmermann and Andreas Friedel},

doi = {10.1007/s11740-021-01084-w},

year  = {2021},

date = {2021-10-19},

journal = {Production Engineering},

abstract = {This paper presents a novel method for a precise localization of the automated-fiber-placement head, without the need for a data access to the machine control. It is based on a sub-pixel accurate optical-flow-algorithm which determines information about the heads movement by means of the material flow in sequences of IR images. Using local curvatures in the temperature field of the IR images, feature matrices are created which can locally be compared to the features of successive images. Thus, the translation between images become visible. This enables the possibility to perform an accurate (16.8μm) and self-sufficient process monitoring that additionally is capable of capturing the motion and position information of the AFP system and can be linked to existing algorithms for defect detection and classification.},

keywords = {Automated Fiber Placement, Process Monitoring},

pubstate = {published},

tppubtype = {article}

}

@article{Budelmann2020,

title = {Prepreg tack: A review of mechanisms, measurement, and manufacturing implication},

author = {Dennis Budelmann and Carsten Schmidt and Dieter Meiners},

doi = {10.1002/pc.25642},

issn = {1548-0569},

year  = {2020},

date = {2020-05-23},

journal = {Polymer Composites},

volume = {2020},

pages = {1-19},

abstract = {The stickiness of prepregs (tack) is considered a decisive material property for the success of high-quality composite manufacturing by automated lay-up processes such as automated fiber placement (AFP) or automated tape laying (ATL). Adverse control of prepreg tack can easily result in laminate defects or machine breakdown, which are highly undesirable considering the tremendous machinery and material costs of these processes. Prepreg tack is governed by a complex interaction of adhesive and cohesive phenomena that are influenced by machine and environmental parameters of the production process

as well as by intrinsic properties of the prepreg material itself. This review aims at providing a condensed insight into the current state of research on prepreg tack. Therefore, experimental studies including the discussion of utilized tack measurement methods as well as model approaches to prepreg tack are reviewed. The findings are discussed against the background of fundamental mechanisms, the strong interdependency of influencing parameters and the challenge of translating measured tack data into an enhanced AFP/ATL process stability by process adjustment.},

keywords = {Automated Fiber Placement, Tack},

pubstate = {published},

tppubtype = {article}

}

@article{Schmidt2019,

title = {Artificial intelligence for non-destructive testing of CFRP prepreg materials},

author = {Carsten Schmidt and Tristan Hocke and Berend Denkena},

url = {https://link.springer.com/article/10.1007%2Fs11740-019-00913-3},

doi = {https://doi.org/10.1007/s11740-019-00913-3},

year  = {2019},

date = {2019-07-02},

journal = {Production Engineering},

pages = {1-10},

abstract = {This paper presents a concept of the quality assurance for CFRP prepreg materials and focusses on the classification of thermographic images using convolution neural networks (CNNs). The method for non-destructive testing of CFRP prepreg materials combines a laser-triangulation sensor and an infrared camera to monitor both, the geometry and the impregnation of the prepreg material. The aim is to ensure a high material quality excluding any defective material in an early stage of the process chain of the production of CFRP components. As a result, the reliability of Automated-Fiber-Placement processes utilizing this previously tested material increases. Therefore, an artificial intelligence is set up to classify the thermal images of the CFRP material. Two different architectures of CNN are trained and validated with data sets consisting of thermal images of several prepreg materials and different material defects, such as geometric deviations and varying fiber-matrix-ratios caused by an incorrect impregnation. The CNNs are able to differentiate prepreg materials and to detect and classify certain material-independent defects for known and trained prepreg materials.},

keywords = {Artificial Intelligence, Automated Fiber Placement, Composite Manufacturing, Composite Structures, Defects, Prepreg, Quality Assurance, Thermal Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Schmidt2019,

title = {Deep learning-based classification of production defects  in automated-fiber-placement processes},

author = {Carsten Schmidt and Tristan Hocke and Berend Denkena},

url = {http://link.springer.com/article/10.1007/s11740-019-00893-4},

doi = {https://doi.org/10.1007/s11740-019-00893-4},

year  = {2019},

date = {2019-03-15},

journal = {Production Engineering},

volume = {13, 3-4},

pages = {501-509},

abstract = {This paper presents a deep learning-based approach for the detection and classification of production defects that comple-

ments an existing thermographic online monitoring system for Automated-Fiber-Placement (AFP) processes. The detection 

and classification procedure is performed in two stages. In the first stage, the system monitors each tow individually and 

classifies its process status. Furthermore, it detects and classifies production defects that affect individual tows such as a 

tow-twist. In the second stage, the system monitors the total width of the faultless tows. In this stage, production defects 

effecting multiple tows, for example gaps or overlaps, are detected and classified. Twelve different deep convolution neural 

networks (CNN) with three various architectures are learned supervised relating to different data sets. The performance of 

both identification stages is explored separately before the entire system will be set up. Therefore, the thermal images of the 

data sets are superimposed by noise to test the performance of the selected CNN.},

keywords = {Automated Fiber Placement, Industry 4.0, Thermal Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Schmidt2018c,

title = {Influence of prepreg material quality on carbon fiber reinforced plastic laminates processed by automated fiber placement},

author = {Carsten Schmidt and Patricc Weber and Tristan Hocke and Berend Denkena},

doi = {https://doi.org/10.1016/j.procir.2017.12.236},

year  = {2018},

date = {2018-10-15},

journal = {Procedia CIRP},

volume = {67},

pages = {422-427},

abstract = {In this paper, the influence of prepreg material quality to the mechanical properties of CFRP parts is presented. During the out life of prepreg slit tape, the processability changes by aging especially when storing the material at room temperature. Therefore, investigations are done with an in-house developed AFP machine and are monitored with a thermal monitoring system to determine the influence of material aging to mechanical properties and changes in the manufacturing process. The manufactured CFRP plates are finally tested by 3-point bending test.},

keywords = {Automated Fiber Placement, Material Aging, Prepreg},

pubstate = {published},

tppubtype = {article}

}

@article{Groß2018,

title = {Production-based design of a hybrid load introduction element for thin-walled CFRP Structures},

author = {Lukas Groß and Alexander Herwig and David C. Berg and Carsten Schmidt and Berend Denkena and Peter Horst and Dieter Meiners},

url = {https://link.springer.com/article/10.1007/s11740-018-0821-4},

doi = {https://doi.org/10.1007/s11740-018-0821-4},

issn = {1863-7353},

year  = {2018},

date = {2018-03-21},

journal = {Production Engineering},

pages = {8},

abstract = {The project “Multi-Layer Inserts” (MLI) proposes a new design for inserts used in thin-walled CFRP structures. The proposed inserts consist of multiple thin metal sheets and is build up simultaneously with the laminate in an intrinsic hybridization process, eliminating time-consuming post-processing steps. Furthermore, at equal weight, such inserts greatly increase the bonding area between metal and CFRP in comparison to conventional inserts. This results in a significant increase of the loads that can be transmitted into the CFRP. The present work discusses how the shape of the metal sheets which the proposed inserts consist of influences the mechanical properties of the surrounding laminate. This influence is investigated by measuring the strain distribution during tensile tests by means of digital image correlation. The strain distributions around the following three different MLI design approaches are compared: An elliptical metal sheet, which is expected to be ideal in terms of mechanical performance of the overall structure; a cross-shape metal sheet representing a production-driven simplification which only requires the ability to perform cuts in individual tows perpendicular to the laying direction and can be performed by state-of-the-art AFP systems; and lastly, a compromise between manufacturability and achieved mechanical performance, a decagonal metal sheet design, which requires angled cuts of the fiber tows. It is shown, that the decagon is able to evenly spread the strain over a larger area and is therefore able to significantly reduce the maximum strain values compared to a cross-shape metal sheet, while still being automatable.},

keywords = {Automated Fiber Placement, Composite Structures, Embedded Load Introduction Element, Fiber-Metal Laminate, Multilayer Insert},

pubstate = {published},

tppubtype = {article}

}

@article{Denkena2016b,

title = {Automated Fiber Placement Head for Manufacturing of Innovative Aerospace Stiffening Structures},

author = {Berend Denkena and Carsten Schmidt and Patricc Weber},

url = {http://www.sciencedirect.com/science/article/pii/S2351978916301494},

doi = {http://dx.doi.org/10.1016/j.promfg.2016.11.013},

year  = {2016},

date = {2016-11-21},

journal = {Procedia Manufacturing},

volume = {6},

pages = {96-104},

abstract = {In the research project “High-performance Production of CFRP Structures” (HP CFK) a new automated fiber placement (AFP) system for laying thermoset CFRP (carbon fiber-reinforced plastic) slit tapes was developed. Its novel, modular designed laying head faces current industrial needs and challenges of prospective carbon light weight applications, e. g. future aerospace stiffening structures. Thus, its compaction unit is optimized for producing complex-curved structures. To allow approximating slopes on curved geometries, it consists of several height-adjustable rollers which, in addition, are each pressure controlled to enable an individual compacting pressure for the lay-up on materials with different compression strength (e. g. foams, metals). Furthermore, the design of the laying heads cutting unit aided the manufacturing of complex structures while being located as near as possible to the nip point to allow very short minimum placement paths. This paper introduces into the general design of the modular laying head as well as preliminary results of validation studies regarding several process limits.},

keywords = {Automated Fiber Placement, Light Weight Construction, Manufacturing Quality, Prepreg},

pubstate = {published},

tppubtype = {article}

}

@article{Schmidt2016b,

title = {Bahnplanung für Automated-Fiber-Placement-Prozesse},

author = {Carsten Schmidt and Klaas Völtzer and Tristan Hocke and Jan Brüning},

year  = {2016},

date = {2016-09-15},

journal = {Lightweight Design},

volume = {4},

keywords = {Automated Fiber Placement, Path Planning, Process Monitoring, Thermal Imaging},

pubstate = {published},

tppubtype = {article}

}