The Experts below are selected from a list of 1143 Experts worldwide ranked by ideXlab platform
Luise Kärger - One of the best experts on this subject based on the ideXlab platform.
-
the impact of Draping effects on the stiffness and failure behavior of unidirectional non crimp fabric fiber reinforced composites
Materials, 2020Co-Authors: Eckart Kunze, Siegfried Galkin, R Bohm, Maik Gude, Luise KärgerAbstract:Unidirectional non-crimp fabrics (UD-NCF) are often used to exploit the lightweight potential of continuous fiber reinforced plastics (CoFRP). During the Draping Process, the UD-NCF fabric can undergo large deformations that alter the local fiber orientation, the local fiber volume content (FVC) and create local fiber waviness. Especially the FVC is affected and has a large impact on the mechanical properties. This impact, resulting from different deformation modes during Draping, is in general not considered in composite design Processes. To analyze the impact of different Draping effects on the mechanical properties and the failure behavior of UD-NCF composites, experimental results of reference laminates are compared to the results of laminates with specifically induced Draping effects, such as non-constant FVC and fiber waviness. Furthermore, an analytical model to predict the failure strengths of UD laminates with in-plane waviness is introduced. The resulting stiffness and strength values for different FVC or amplitude to wavelength configurations are presented and discussed. In addition, failure envelopes based on the PUCK failure criterion for each Draping effect are derived, which show a clear specific impact on the mechanical properties. The findings suggest that each Draping effect leads to a “new fabric” type. Additionally, analytical models are introduced and the experimental results are compared to the predictions. Results indicate that the models provide reliable predictions for each Draping effect. Recommendations regarding necessary tests to consider each Draping effect are presented. As a further prospect the resulting stiffness and strength values for each Draping effect can be used for a more accurate prediction of the structural performance of CoFRP parts.
-
Experimental and numerical investigation of the contact behavior during FE forming simulation of continuously reinforced composites in wet compression molding
PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019, 2019Co-Authors: Christian Poppe, Dominik Dörr, Fabian Kraus, Luise KärgerAbstract:Wet compression molding (WCM) provides large-scale production potential for continuously fiber-reinforced structural components due to simultaneous infiltration and Draping during molding (viscous Draping). Experimental and theoretical investigations proved strong mutual dependencies between resin progression and textile Draping. Significant cavity pressures only develop towards the end of the tool stroke, when the cavity is almost filled with resin and fibers. Therefore, the resin’s impact on the Draping behavior (intra-ply and interface behavior) is of great relevance, since it represents a large share of the Draping Process. This study extends the work presented by Huttl et al. [1] with experimental interface tests on dry and infiltrated woven fabrics, which confirm rate-, pressure- and viscosity-dependent tangential contact behavior within the viscous Draping stage. Furthermore, experimental results for the ply-ply interface are utilized to parametrizes a contact model, which is subsequently applied to assess and evaluate the Process relevance on part level by means of FE forming simulation. Although a relatively simple geometry has been investigated, numerical results show a significant impact of the infiltration-depended tangential contact formulation on part level. Beyond that, an investigation of the ply-ply contact state (pressure or tension) reveal that transversal pressure is only predominate towards the end of the tool stroke. Consequently, contact characterization and parametrization should also include tests at low transversal pressure.Wet compression molding (WCM) provides large-scale production potential for continuously fiber-reinforced structural components due to simultaneous infiltration and Draping during molding (viscous Draping). Experimental and theoretical investigations proved strong mutual dependencies between resin progression and textile Draping. Significant cavity pressures only develop towards the end of the tool stroke, when the cavity is almost filled with resin and fibers. Therefore, the resin’s impact on the Draping behavior (intra-ply and interface behavior) is of great relevance, since it represents a large share of the Draping Process. This study extends the work presented by Huttl et al. [1] with experimental interface tests on dry and infiltrated woven fabrics, which confirm rate-, pressure- and viscosity-dependent tangential contact behavior within the viscous Draping stage. Furthermore, experimental results for the ply-ply interface are utilized to parametrizes a contact model, which is subsequently applied t...
-
Development of a Modular Draping Test Bench for Analysis of Infiltrated Woven Fabrics in Wet Compression Molding
Key Engineering Materials, 2019Co-Authors: Fabian Albrecht, Clemens Zimmerling, Luise Kärger, Christian Poppe, Frank HenningAbstract:The wet compression molding (WCM) Process enables short cycle times for production of fiber-reinforced plastics due to simultaneous infiltration, viscous Draping and consolidation in one Process step. This requires a comprehensive knowledge of occurring mutual dependencies in particular for the development of Process simulation methods and for Process optimization. In this context, it is necessary to develop suitable test benches to enable an evaluation of the outlined viscous Draping behavior. In order to evaluate and suitably design the Draping Process, grippers are mounted on a surrounding frame, which enables targeted restraining of the local material draw-in during forming. In supporting the development of the new test bench, first experimental and simulation results are compared, which thereby enables a first validation of the simulation approaches. Results show a good agreement between experimental and numerical results in terms of shear deformation and final gripper displacement under dry and viscous conditions. Results recommend that future development for investigations of viscous Draping effects should focus an enabling measurement of gripper displacement during the forming Process. Beyond that, the modular test bench design enables experimental and virtual Draping optimization and deduction of blank holder concepts for WCM tools.
-
Application and Evaluation of Meta-Model Assisted Optimisation Strategies for Gripper-Assisted Fabric Draping in Composite Manufacturing
2018Co-Authors: Clemens Zimmerling, Julius Pfrommer, Jinzhao Liu, Jürgen Beyerer, Frank Henning, Luise KärgerAbstract:With respect to their extraordinary weight-specific mechanical properties, continuous Fibre Reinforced Plastics (CoFRP) have drawn increasing attention for use in load bearing structures. Contrasting metals, manufacturing of CoFRPs components requires multiple steps, often including a Draping Process of textiles. To predict and optimise the manufacturing Process, Finite-Element (FE) simulation methods are being developed along virtual Process chains. For maximum part quality, Draping Process parameters need to be optimised, which requires numerous computationally expensive iterations. While efforts have been made for time-efficient Process optimisation in metal forming, composite Draping optimisation has is a comparably young discipline and still lacks time-efficient optimisation strategies. In this work, modelling strategies for time-efficient optimisation using computationally inexpensive meta-models are examined, which are used to guide the search for optima in the parameter space. The meta-models are trained by observations of FE-based Draping simulations of an automotive part, thereby learning the relationship between variable gripper forces (input) and the resulting shear angles (output). Parametric model functions are compared against deep neural networks (DNN) as non-parametric models with respect to prediction accuracy. Best results are achieved using a DNN that predicts the shear angles of more than 24 000 fabric shell elements.
-
Optimisation of manufacturing Process parameters using deep neural networks as surrogate models
Procedia CIRP, 2018Co-Authors: Julius Pfrommer, Clemens Zimmerling, Jinzhao Liu, Frank Henning, Luise Kärger, Jürgen BeyererAbstract:Abstract Optimisation of manufacturing Process parameters requires resource-intensive search in a high-dimensional parameter space. In some cases, physics-based simulations can replace actual experiments. But they are computationally expensive to evaluate. Surrogate-based optimisation uses a simplified model to guide the search for optimised parameter combinations, where the surrogate model is iteratively improved with new observations. This work applies surrogate-based optimisation to a composite textile Draping Process. Numerical experiments are conducted with a Finite Element (FE) simulation model. The surrogate model, a deep artificial neural network, is trained to predict the shear angle of more than 24,000 textile elements. Predicting detailed Process results instead of a single performance scalar improves the model quality, as more relevant data from every experiment can be used for training. For the textile Draping case, the approach is shown to reduce the number of resource-intensive FE simulations required to find optimised parameter configurations. It also improves on the best-known overall solution.
Homayoun Najjaran - One of the best experts on this subject based on the ideXlab platform.
-
ICSM - Robot-Assisted Composite Manufacturing Based on Machine Learning Applied to Multi-view Computer Vision
Lecture Notes in Computer Science, 2020Co-Authors: Abtin Djavadifar, Marian Korber, John Brandon Graham-knight, Kashish Gupta, Patricia Lasserre, Homayoun NajjaranAbstract:This paper introduces an automated wrinkle detection method on semi-finished fiber products in the aerospace manufacturing industry. Machine learning, computer vision techniques, and evidential reasoning are combined to detect wrinkles during the Draping Process of fibre-reinforced materials with an industrial robot. A well-performing Deep Convolutional Neural Network (DCNN) was developed based on a preliminary, hand-labelled dataset captured on a functioning robotic system used in a composite manufacturing facility. Generalization of this model to different, unlearned wrinkle features naturally compromises detection accuracy. To alleviate this problem, the proposed method employs computer vision techniques and belief functions to enhance defect detection accuracy. Co-temporal views of the same fabric are extracted, and individual detection results obtained from the DCNN are fused using the Dempster-Shafer Theory (DST). By the application of the DST rule of combination, the overall wrinkle detection accuracy for the generalized case is greatly improved in this composite manufacturing facility.
-
Wrinkle and boundary detection of fiber products in robotic composites manufacturing
Assembly Automation, 2019Co-Authors: Kashish Gupta, Marian Korber, Abtin Djavadifar, Florian Krebs, Homayoun NajjaranAbstract:The paper aims to focus on a vision-based approach to advance the automated Process of the manufacturing of an Airbus A350’s pressure bulkhead. The setup enables automated deformation and Draping of a fiber textile on a form-variable end-effector.,The proposed method uses the information of infrared (IR) and color-based images in Red, Green and Blue (RGB) representative format, as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The technique implements a simple and practical image Processing solution using a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution, thereby demonstrating practical and applied research.,The efficacy of the technique is demonstrated via experiments in the presented work. The two objectives as follows boundary edge detection and wrinkle detection are accomplished in real time in an industrial setup.,During the Draping Process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production Process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The proposed work aids in alleviating the problems caused by these wrinkles and helps in quality control in the production Process.
-
CASE - Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018Co-Authors: Kashish Gupta, Marian Korber, Florian Krebs, Homayoun NajjaranAbstract:The paper focuses on a vision-based approach for optimizing automated deformation and Draping Processes of dry semi-finished fiber products at the production of large-area composite components for the aerospace industry. The vision-based approach developed at University of British Columbia, is to be utilized with the existing Draping Process, carried out on a form-variable end-effector, developed at the Center for Lightweight Production Technologies (ZLP) in Augsburg. During the deformation of the semi-finished product, tensions develop in the material leading to shearing and relative movements of the fiber material on the gripping surface. In turn, the resulting displacement and deformation of the cut piece negatively influences the production quality. The method proposed in the paper is designed to help in visually detecting and automatically evaluating the drape and deformation of the cut piece on a laboratory scale setup. For this purpose, RGB-D camera data is used to detect the deformed gripper surface and determine the position, the boundary geometry and any wrinkles that may have occurred in the cut piece. The accuracy of the proposed method is verified by experiments on a known target geometry.
Frank Henning - One of the best experts on this subject based on the ideXlab platform.
-
Development of a Modular Draping Test Bench for Analysis of Infiltrated Woven Fabrics in Wet Compression Molding
Key Engineering Materials, 2019Co-Authors: Fabian Albrecht, Clemens Zimmerling, Luise Kärger, Christian Poppe, Frank HenningAbstract:The wet compression molding (WCM) Process enables short cycle times for production of fiber-reinforced plastics due to simultaneous infiltration, viscous Draping and consolidation in one Process step. This requires a comprehensive knowledge of occurring mutual dependencies in particular for the development of Process simulation methods and for Process optimization. In this context, it is necessary to develop suitable test benches to enable an evaluation of the outlined viscous Draping behavior. In order to evaluate and suitably design the Draping Process, grippers are mounted on a surrounding frame, which enables targeted restraining of the local material draw-in during forming. In supporting the development of the new test bench, first experimental and simulation results are compared, which thereby enables a first validation of the simulation approaches. Results show a good agreement between experimental and numerical results in terms of shear deformation and final gripper displacement under dry and viscous conditions. Results recommend that future development for investigations of viscous Draping effects should focus an enabling measurement of gripper displacement during the forming Process. Beyond that, the modular test bench design enables experimental and virtual Draping optimization and deduction of blank holder concepts for WCM tools.
-
Application and Evaluation of Meta-Model Assisted Optimisation Strategies for Gripper-Assisted Fabric Draping in Composite Manufacturing
2018Co-Authors: Clemens Zimmerling, Julius Pfrommer, Jinzhao Liu, Jürgen Beyerer, Frank Henning, Luise KärgerAbstract:With respect to their extraordinary weight-specific mechanical properties, continuous Fibre Reinforced Plastics (CoFRP) have drawn increasing attention for use in load bearing structures. Contrasting metals, manufacturing of CoFRPs components requires multiple steps, often including a Draping Process of textiles. To predict and optimise the manufacturing Process, Finite-Element (FE) simulation methods are being developed along virtual Process chains. For maximum part quality, Draping Process parameters need to be optimised, which requires numerous computationally expensive iterations. While efforts have been made for time-efficient Process optimisation in metal forming, composite Draping optimisation has is a comparably young discipline and still lacks time-efficient optimisation strategies. In this work, modelling strategies for time-efficient optimisation using computationally inexpensive meta-models are examined, which are used to guide the search for optima in the parameter space. The meta-models are trained by observations of FE-based Draping simulations of an automotive part, thereby learning the relationship between variable gripper forces (input) and the resulting shear angles (output). Parametric model functions are compared against deep neural networks (DNN) as non-parametric models with respect to prediction accuracy. Best results are achieved using a DNN that predicts the shear angles of more than 24 000 fabric shell elements.
-
Optimisation of manufacturing Process parameters using deep neural networks as surrogate models
Procedia CIRP, 2018Co-Authors: Julius Pfrommer, Clemens Zimmerling, Jinzhao Liu, Frank Henning, Luise Kärger, Jürgen BeyererAbstract:Abstract Optimisation of manufacturing Process parameters requires resource-intensive search in a high-dimensional parameter space. In some cases, physics-based simulations can replace actual experiments. But they are computationally expensive to evaluate. Surrogate-based optimisation uses a simplified model to guide the search for optimised parameter combinations, where the surrogate model is iteratively improved with new observations. This work applies surrogate-based optimisation to a composite textile Draping Process. Numerical experiments are conducted with a Finite Element (FE) simulation model. The surrogate model, a deep artificial neural network, is trained to predict the shear angle of more than 24,000 textile elements. Predicting detailed Process results instead of a single performance scalar improves the model quality, as more relevant data from every experiment can be used for training. For the textile Draping case, the approach is shown to reduce the number of resource-intensive FE simulations required to find optimised parameter configurations. It also improves on the best-known overall solution.
-
Development and validation of a CAE chain for unidirectional fibre reinforced composite components
Composite Structures, 2015Co-Authors: Luise Kärger, Alexander Bernath, Florian Fritz, Siegfried Galkin, Dino Magagnato, André Oeckerath, Alexander Schön, Frank HenningAbstract:Abstract Current development of composite components made by Resin Transfer Moulding (RTM) requires numerous manual iteration steps to find the optimal design in conjunction with optimal Process control. Still, such components are often highly oversized since the real material behaviour is influenced by the Processing history and cannot be sufficiently predicted by simulations. The Draping Process is the predominating Process for the fibre alignments, resulting in varying fibre orientations and local Draping effects. These material characteristics influence the moulding Process as well as the mechanical performance and need to be considered for sizing and virtual validation of RTM structures. Therefore, a continuous virtual Process chain (CAE chain) is developed in this work, where geometry and material data are transferred between the finite element models by using a neutral exchange format and mapping algorithms. The CAE chain is applied and validated by a complexly curved RTM part. To demonstrate the benefit of the CAE chain, a reference model is used, where the fibre orientations are simply projected to the component’s surface. For experimental validation, the simulation results are compared to pressure and temperature measurements in the case of moulding simulation, and to tension and bending tests in the case of structural simulation.
Marian Korber - One of the best experts on this subject based on the ideXlab platform.
-
ICSM - Robot-Assisted Composite Manufacturing Based on Machine Learning Applied to Multi-view Computer Vision
Lecture Notes in Computer Science, 2020Co-Authors: Abtin Djavadifar, Marian Korber, John Brandon Graham-knight, Kashish Gupta, Patricia Lasserre, Homayoun NajjaranAbstract:This paper introduces an automated wrinkle detection method on semi-finished fiber products in the aerospace manufacturing industry. Machine learning, computer vision techniques, and evidential reasoning are combined to detect wrinkles during the Draping Process of fibre-reinforced materials with an industrial robot. A well-performing Deep Convolutional Neural Network (DCNN) was developed based on a preliminary, hand-labelled dataset captured on a functioning robotic system used in a composite manufacturing facility. Generalization of this model to different, unlearned wrinkle features naturally compromises detection accuracy. To alleviate this problem, the proposed method employs computer vision techniques and belief functions to enhance defect detection accuracy. Co-temporal views of the same fabric are extracted, and individual detection results obtained from the DCNN are fused using the Dempster-Shafer Theory (DST). By the application of the DST rule of combination, the overall wrinkle detection accuracy for the generalized case is greatly improved in this composite manufacturing facility.
-
Wrinkle and boundary detection of fiber products in robotic composites manufacturing
Assembly Automation, 2019Co-Authors: Kashish Gupta, Marian Korber, Abtin Djavadifar, Florian Krebs, Homayoun NajjaranAbstract:The paper aims to focus on a vision-based approach to advance the automated Process of the manufacturing of an Airbus A350’s pressure bulkhead. The setup enables automated deformation and Draping of a fiber textile on a form-variable end-effector.,The proposed method uses the information of infrared (IR) and color-based images in Red, Green and Blue (RGB) representative format, as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The technique implements a simple and practical image Processing solution using a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution, thereby demonstrating practical and applied research.,The efficacy of the technique is demonstrated via experiments in the presented work. The two objectives as follows boundary edge detection and wrinkle detection are accomplished in real time in an industrial setup.,During the Draping Process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production Process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The proposed work aids in alleviating the problems caused by these wrinkles and helps in quality control in the production Process.
-
automated planning and optimization of a Draping Processes within the catia environment using a python software tool
Procedia Manufacturing, 2019Co-Authors: Marian Korber, Christoph FrommelAbstract:Abstract This thesis deals with the challenge of the virtual generation and optimization of complex production Processes within the Computer Aided Design (CAD) environment CATIA V5. A flexible robot end-effector –(Modular Gripper) is used for automated Draping Process which serves as an example application. Previous experiments have shown that manually selecting all Process parameters that are needed to adapt the grippers geometry is a very time consuming and inaccurate method. For this reason, a tool was developed that is able to determine or optimize the end-effector specific Process parameters offline. Since all existing models are available in the CATIA environment, the goal was to perform this optimization Process within the same environment. The software tool is based on a hierarchical architecture where all calculations and logic optimization Processes are Processed in the programming language Python. All commands in the CATIA environment are performed with the help of small, modularized CATScript programs that are able to interact with main Python algorithms. This work is intended to lead to a virtual optimization Process that can be universally applied to complex manufacturing Processes. Since it is based on a Python framework that is able to interact directly with the CATIA environment, it is flexible enough so that it can be adapted to further Processes.
-
EVALUATING THE Draping QUALITY OF MECHANICAL PREFORMED CARBON FIBRE TEXTILES FOR DOUBLE CURVED GEOMETRIES
2018Co-Authors: Christoph Frommel, Marian Korber, Marcin Malecha, Monika Mayer, Alfons Schuster, Mark WillmerothAbstract:Manufacturing of large carbon fibre reinforced plastic (CFRP) structures needs to be automated to fulfil the rising amount of aircrafts ordered worldwide. The main aspect of using CFRP structures is the low weight associated with high mechanical properties. To achieve these properties the fibre orientation is mandatory. Nevertheless, the complex part of preforming large structures is commonly done by hand due to the difficulty in handling the fragile fabrics. The approach at the DLR to automate this Process is the usage of robotically controlled kinematic endeffectors. To make this Process attractive for manufacturers the quality of the draped cut pieces needs to meet the requirements specified by the design engineers. In this paper a dry carbon fibre cut piece was draped by a kinematic endeffector. The Draping simulation parameters were adjusted from the manual manufacturing to the Draping Process by the endeffector. The draped cut pieces were then measured regarding the placement position via laser scanner and resulting fibre angles by an optical measurement system. Concluding the measured values where compared to the results of the simulation.
-
CASE - Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018Co-Authors: Kashish Gupta, Marian Korber, Florian Krebs, Homayoun NajjaranAbstract:The paper focuses on a vision-based approach for optimizing automated deformation and Draping Processes of dry semi-finished fiber products at the production of large-area composite components for the aerospace industry. The vision-based approach developed at University of British Columbia, is to be utilized with the existing Draping Process, carried out on a form-variable end-effector, developed at the Center for Lightweight Production Technologies (ZLP) in Augsburg. During the deformation of the semi-finished product, tensions develop in the material leading to shearing and relative movements of the fiber material on the gripping surface. In turn, the resulting displacement and deformation of the cut piece negatively influences the production quality. The method proposed in the paper is designed to help in visually detecting and automatically evaluating the drape and deformation of the cut piece on a laboratory scale setup. For this purpose, RGB-D camera data is used to detect the deformed gripper surface and determine the position, the boundary geometry and any wrinkles that may have occurred in the cut piece. The accuracy of the proposed method is verified by experiments on a known target geometry.
Kashish Gupta - One of the best experts on this subject based on the ideXlab platform.
-
ICSM - Robot-Assisted Composite Manufacturing Based on Machine Learning Applied to Multi-view Computer Vision
Lecture Notes in Computer Science, 2020Co-Authors: Abtin Djavadifar, Marian Korber, John Brandon Graham-knight, Kashish Gupta, Patricia Lasserre, Homayoun NajjaranAbstract:This paper introduces an automated wrinkle detection method on semi-finished fiber products in the aerospace manufacturing industry. Machine learning, computer vision techniques, and evidential reasoning are combined to detect wrinkles during the Draping Process of fibre-reinforced materials with an industrial robot. A well-performing Deep Convolutional Neural Network (DCNN) was developed based on a preliminary, hand-labelled dataset captured on a functioning robotic system used in a composite manufacturing facility. Generalization of this model to different, unlearned wrinkle features naturally compromises detection accuracy. To alleviate this problem, the proposed method employs computer vision techniques and belief functions to enhance defect detection accuracy. Co-temporal views of the same fabric are extracted, and individual detection results obtained from the DCNN are fused using the Dempster-Shafer Theory (DST). By the application of the DST rule of combination, the overall wrinkle detection accuracy for the generalized case is greatly improved in this composite manufacturing facility.
-
Wrinkle and boundary detection of fiber products in robotic composites manufacturing
Assembly Automation, 2019Co-Authors: Kashish Gupta, Marian Korber, Abtin Djavadifar, Florian Krebs, Homayoun NajjaranAbstract:The paper aims to focus on a vision-based approach to advance the automated Process of the manufacturing of an Airbus A350’s pressure bulkhead. The setup enables automated deformation and Draping of a fiber textile on a form-variable end-effector.,The proposed method uses the information of infrared (IR) and color-based images in Red, Green and Blue (RGB) representative format, as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The technique implements a simple and practical image Processing solution using a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution, thereby demonstrating practical and applied research.,The efficacy of the technique is demonstrated via experiments in the presented work. The two objectives as follows boundary edge detection and wrinkle detection are accomplished in real time in an industrial setup.,During the Draping Process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production Process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The proposed work aids in alleviating the problems caused by these wrinkles and helps in quality control in the production Process.
-
CASE - Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018Co-Authors: Kashish Gupta, Marian Korber, Florian Krebs, Homayoun NajjaranAbstract:The paper focuses on a vision-based approach for optimizing automated deformation and Draping Processes of dry semi-finished fiber products at the production of large-area composite components for the aerospace industry. The vision-based approach developed at University of British Columbia, is to be utilized with the existing Draping Process, carried out on a form-variable end-effector, developed at the Center for Lightweight Production Technologies (ZLP) in Augsburg. During the deformation of the semi-finished product, tensions develop in the material leading to shearing and relative movements of the fiber material on the gripping surface. In turn, the resulting displacement and deformation of the cut piece negatively influences the production quality. The method proposed in the paper is designed to help in visually detecting and automatically evaluating the drape and deformation of the cut piece on a laboratory scale setup. For this purpose, RGB-D camera data is used to detect the deformed gripper surface and determine the position, the boundary geometry and any wrinkles that may have occurred in the cut piece. The accuracy of the proposed method is verified by experiments on a known target geometry.
