bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-Time Capable Architecture for Software-Defined Manufacturing
Authors: Oechsle Stefan , Walker Moritz , Fischer Marc , Frick Florian , Lechler Armin , Verl Alexander 
Published: June 2023

AbstractProduction systems are characterized by static configurations and slow adaption to changing requirements. They no longer meet current trends in mutability and dynamic adaptation. Software-defined Manufacturing (SDM) like other software-defined approaches leverages abstraction of hardware to achieve higher flexibility. Based on abstracted hardware, software defines desired functionalities. Requirements from the Operational Technology (OT), especially determinism, must be combined with the flexibility and interoperability of Information Technology (IT). This paper proposes a stack that enables the implementation of SDM based on a requirements analysis. It covers the main phases of the life cycle of automation applications and additional requirements from SDM. We derive the necessary components while resorting to existing approaches whenever possible. Means for applications engineering, configuration, deployment, and orchestration, as well as execution at run time, are developed.
Influence of the Joining Force on the Nugget Diameter During Resistance Spot Welding of Aluminum Materials
Authors: Pestka Jonas , Weihe Stefan 
Published: June 2023

AbstractEver new requirements for the environmental compatibility and safety of products do not stop at the automotive industry. This results in ever shorter time windows for the pre-series processes. For this reason, time-consuming preliminary tests must be minimized to ensure an accelerated start-up and production that is as trouble-free as possible. This maxim applies to all parts of the production of an automobile, but especially to the body shop, where just-in-time production has been the norm for years and any downtimes are particularly severe. In order to meet these requirements, it is advantageous to understand or predict the result of a production process as well as possible.This paper deals with resistance spot welding of aluminum and aims to generate knowledge about the relationship between setting parameters and target value. Since resistance spot welding has a large number of setting parameters and a comprehensive investigation of these would go beyond the scope of this study, the focus is on the joining force and the features to be generated from it. The target parameter is the nugget diameter of the joining points. The data are determined experimentally and evaluated with the help of Machine Learning (ML) approaches. The correlations are presented in graphical form. The quantification of the influence of the joining force on the nugget diameter is expected to lead to a reduction of tests and thus to a gain in time.
Towards an Automated System for Robot Assembly Cell Layout Optimization
Authors: Beck Joshua 
Published: June 2023

AbstractThe manufacturing industry is exposed to various challenges: Increasing competition from companies abroad, the growing unpredictability and volatility of markets, and rising customer demands forcing manufacturers to be highly flexible and offer low-cost products. Using simulation tools, productions systems can be planned and analyzed before the deployment in the field, reducing the commissioning of named systems and avoiding cost-intensive errors. However, manual generation and validation of simulation models is a tedious task and demands expert knowledge of the planners about the simulations tools used. Automated generation of simulation models promises to reduce further commissioning time, making simulation tools more cost-effective. This paper compares existing approaches for 3D simulation generation and/or validation and further presents a concept for automated generation of a 3D model of a robot cell, creating a simulation and validating the processes taking place with minimum user intersection to accelerate robot cell commissioning. This is done by combining layout information with assembly related information extracted directly from 3D product model.
Adaptive Intralogistics with Low-Cost AGVs for a Modular Production System
Authors: Stillig Javier , Brenner Carolin , Colomb André 
Published: June 2023

AbstractDue to ever faster changing market requirements, industrial production equipment needs to become much more flexible. For this reason, the Institute of Mechanical Handling and Logistics and the Institute of Electrical Energy Conversion are developing versatile, automated, and easily adaptable solutions to increase the flexibility of future intralogistics systems. As part of the ANTS 4.0 research project, a modular low-cost automated guided vehicle has been created, which breaks down the flow of goods into its smallest units: A small load carrier. The vehicle is prepared to be charged inductively and guided by color coded LED strips inside the floor, controlled from a superordinated artificial intelligence algorithm. In case of finding an obstacle by the object detection integrated in the floor, the route is recalculated and adapted in real-time.
Performance Comparison of Supervised and Reinforcement Learning Approaches for Separating Entanglements in a Bin-Picking Application
Authors: Moosmann Marius , Kaiser Manuel , Rosport Johannes , Spenrath Felix , Kraus Werner , Bormann Richard , Huber Marco F. 
Published: June 2023

AbstractMachine Learning helps to separate entanglements in Bin-Picking Applications. The goal is to create a system that finds a path to separate an entanglement, starting from a single visual input. To realize such a system both supervised and reinforcement learning methods can be implemented. For both of these approaches we set up a motion model and the remaining properties of the real robot cell are implemented in a simulation scene. While the simulation scene can be used to create training data for the supervised learning approach, it is also the learning environment for the reinforcement learning model. Therefore, there are similar premises for comparing the two models. What needs to be investigated is which of the two methods separates the most entanglements and offers the least setup effort. The setup effort in general and the performance are examined for both approaches in simulation and also in real-world experiments. The reinforcement learning model outperforms both of the supervised learning models in the setup effort and the separation rate by over 15 percent points.
Machine Learning-Based Identification of Root Causes for Defective Units in Manufacturing Processes
Authors: Rotter Dominik , Liebgott Florian , Kessler Daniel , Liebgott Annika , Yang Bin 
Published: June 2023

AbstractTo achieve a high overall equipment effectiveness in a manufacturing process, reducing the number of defective units is crucial. It is therefore vital to identify the root causes of defects to be able to rectify them. However, the analysis of defective units can be a time-consuming and costly task.By using machine learning, we can leverage data of the manufacturing process, like process states and different measurement values, to identify the root causes for the defects. We propose to use this data as features for the classification of a unit as defective or as belonging to a specific defect class. We can then identify the root causes for the defects by calculating the importance of the features.In this paper, we compare our feature-based approach with deep learning methods based on the attention mechanism. The evaluation of our approach on data of a complex production process shows, that our approach clearly outperforms the deep learning methods. It also revealed the challenges in the collection of meaningful data
Enabling Single-Sensor Simultaneous Condition Monitoring of Several Vibration-Emitting Machine Parts Using Neural Networks
Authors: Seitz Andreas , Liebgott Florian , Rotter Dominik , Kessler Daniel , Beise Hans-Peter 
Published: June 2023

AbstractIn production environments, monitoring the vibration of a machine or parts thereof can yield important information about the condition of the machine. The most common recommendation for vibration-based condition monitoring is to place a vibration sensor on each part of interest. These vibration sensors usually output preprocessed data, for example the root mean square value of a predefined time window.We propose to use machine learning to simultaneously monitor several vibration-emitting machine parts using only one single vibration sensor. Due to the superposition of multiple vibrations, this is not feasible using preprocessed sensor data. Our approach therefore consists of a one-dimensional convolutional neural network, which uses the raw vibration signal as input to classify the status of the monitored machine parts.As a first proof of concept, we monitored the status of three different motors. Using our approach, we were able to detect, which motors were running at a given time with high accuracy. We were able to significantly improve the classification results by transforming the raw data into the frequency domain. Our results are promising and show, that monitoring several vibration-emitting machine parts at the same time using only one vibration sensor is feasible.
Combination of the Scanning and the Polar Measuring Method for the Defect Detection in Dry Fibre Layups Using an Eddy Current Sensor
Authors: Mimra Christopher , Eisenbart Boris , Carosella Stefan , Middendorf Peter 
Published: June 2023

AbstractGaps, fibre misalignment and foreign objects in dry fibre tape layups are detrimental towards the final mechanical properties of carbon fibre composite parts. Eddy current sensors can be used to inspect layups for the named defects. In comparison to optical inspection methods, eddy current measurements are capable of inspecting not only the top layer but multiple plies at once. In the literature, two different methods have been proposed for this inspection. One is the scanning method, where the sensor is moved over the specimen and creates a grey-scale image. The pixel brightness correlates with the layup’s local electrical conductivity. The other method for the local inspection is the polar method, where the sensor is rotated on a single spot to detect the fibre orientation. In this work, both methods were conducted with the same inspection system, which is based on a collaborative robotic arm. It has been shown that the scanning method can identify gaps and foreign objects out of metal. The fibre orientation can be identified by the rotational method with a precision of 0.16$$^{\circ }$$

∘

. The exact positioning of the probe remains the main challenge for this inspection system. However, a combination of both methods promises to provide a reliable inspection method for the most common defect in a dry fibre layup.
Detection of Gaps and Overlaps in Laser Line Triangulation Data of Dry Fibre Tape Layups Using Image Projection
Authors: Mimra Christopher , Krein Vincent , Radjef Racim , Fox Bronwyn , Middendorf Peter 
Published: June 2023

AbstractA major cost driver in the production of carbon composite parts is the quality inspection, which to this day relies on manual investigation by a trained worker. Gaps and overlaps in the layups are to be detected because they are proven to be detrimental towards the mechanical properties of the final part. In recent works, laser line triangulation sensors have been applied to inspect layups of prepreg tapes and non-crimp fabric material. These sensors create a 3D point cloud of the specimens surface. This is then evaluated by conversion into a grey-scale image and a subsequent image processing algorithm. However, the most commonly used algorithms fail to differentiate between defects and small, acceptable irregularities, such as welding spots, slits and single fibres which stick out.The aim of this research is to develop a reliable evaluation method for scans of dry fibre tape layups. An overview over the different groups of algorithms is provided, image projection is selected and compared to algorithms which have been proven to work best on pre-pregs. While the common algorithms fail to classify a test set of dry fibre specimen, image projection can reach a true positive rate of 100% and a false positive rate of 19%. The proposed setup can be a centrepiece of a future in-line quality inspection system for dry fibre layups which has potential for a significant decrease of the manufacturing costs.
Parameter Study and Optimization of Forming Simulations for Tape-Based Fiber Layups
Authors: Saeed Muhammad S. , Gugliuzza Jakob M. J. , Liebl Michael , Eisenbart Boris , Radjef Racim , Middendorf Peter , Kreimeyer Matthias 
Published: June 2023

AbstractThis work presents a model of a parametrized fiber-reinforced tapes layup for simulations of stamp forming. BETA CAE Systems’ ANSA v22.1.0 produced the forming model. The initial configurations of the forming process are simulated using LS-DYNA R12 from the Livermore Software Technology Corporation (LSTC) on the High-Performance Computing Center Stuttgart (HLRS). A doubledome geometry was used as a use case in the presented study. An optimal solution is found by predicting and analyzing the forming defects, both quantitatively and qualitatively. Typical defects induced by the forming process include wrinkles, bridging, waviness, misalignment of tapes, instabilities due to tape overlaps and gaps, etc. Adjustments to the forming process are made by varying model parameters such as stacking sequence, fiber angle, material properties, tape orientation, or the position of ultrasonic thermal bonds between tapes. In addition, process parameters such as tool velocity, temperature, and gripping force were considered. Different algorithms for the sample creation of the designed experiments are examined, and the influence of various parameters on the final part is discussed. The limitations and requirements of the employed software are examined for additional studies. Furthermore, the machine learning-based neural network algorithms discussed, such as particle swarm optimization (PSO), gradient-based optimization (GBO), response surface method (RSM), and genetic algorithms (GA), may be used in future research to determine an ideal tape arrangement for various geometries based on nodal locations and the parameters as mentioned above.
Analysis of New Concepts for the Consolidation Roller in Laser-Assisted Automated Tape Placement Processes
Authors: Widmaier Nils , Raps Lukas 
Published: June 2023

AbstractThe need for more sustainable production has led to an increased popularity of thermoplastic fibre reinforced composites over the last years. Typical production processes rely on autoclave and/or sequential processes, which lead to long cycle times, high energy consumption and high costs. One step in-situ production processes, like the thermoplastic laser assisted automated fibre placement, provide an ideal solution to these challenges while enabling the benefits of circular economy through improved recyclability. An essential part of the production process is to ensure full consolidation during layup. One of the main influences on consolidation is the applied pressure of the compaction roller. Increasingly complex part geometries with curved surfaces place special demands on the deformation and adaptability of these rollers. Here, current solutions quickly reach their limits. This paper investigates new concepts for the consolidation roller to enable successful use of in-situ placement technologies on complex part geometries. Different sheath thicknesses and materials were investigated in experiments, followed by simulative investigation of further compaction roller concepts.
Development, Implementation and Evaluation of a Prototype System for Data-Driven Optimization of a Preforming Process
Authors: Liebl Michael , Holder Jonas , Mohr Tobias , Dorneich Albert , Liebgott Florian , Middendorf Peter 
Published: June 2023

AbstractModern production of fiber reinforced composites via the preforming process is widely used in the industry. A common way to create dry, semi-finished fiber products is forming or draping a textile into a three-dimensional component geometry. The punch and die process is often used for resin transfer molding (RTM) composite manufacturing. Due to the major influence of the preforming step on the later mechanical performance of the component, a detailed knowledge of the fiber architecture is beneficial.To enable in-situ monitoring of the specific deformation of a woven fabric, a novel kind of single-use two-dimensional strain sensors has already been developed and characterized. We show that by using industrial communication standards, data from various data sources can be consolidated in an edge computer and used to improve the process. To this end, we developed the hardware and firmware of a device that reads out the printed strain sensors and transfers the data to the edge device via IO-Link. In addition, the edge device collects data from a programmable logic controller and is capable of connecting further IO-Link sensors.Our demonstrator is intended as a proof of concept for in-situ monitoring, data-driven analysis and improvement of the punch and die process and will be further developed. We propose a machine learning-based edge analytics approach for detecting defects and increasing the preforming quality during the draping process. Forming tests with the double-dome benchmark geometry and the carbon fabric which is suitable for industry have been carried out to validate our prototype system.
Resource Efficient Manufacturing of Complex Cooling Structures
Authors: John Leonard , Becker Dina , Reuter Steffen , Möhring Hans-Christian , Doppelbauer Martin , Berg Lars F. 
Published: June 2023

AbstractThe combination of additive manufacturing (LPBF) and plastic injection molding offers great benefits for functional integration and thereby the functionality of parts, components and systems. By manufacturing cooling channels as very thin-walled structures, short laser processing time is needed to produce these parts. The combination with plastic overmolding allows the mechanical loads to be absorbed, while at the same time utilizing the freedom of shape of the LPBF process. In order to prevent the structures from collapsing during overmolding or to ensure that they do not have to be very thick-walled in order to withstand the high injection pressures, the structures are closed, so that the base-powder is still contained in the structure during the overmolding. After opening the overmolded components, the powder can be extracted and reused. This technique offers great potential for high functional integration and new possibilities in terms of realizing internal cooling structures of electric traction machines. Other advantages that can be exploited are the thermally good connection due to the material bond and the possibility of local reinforcement due to the design freedom of the insert. Thanks to the injection-molded component, complex geometries can also be produced without additional unit costs.
Software-Defined Manufacturing for the Entire Life Cycle at Different Levels of Production
Authors: Behrendt Sebastian , Martin Michael , Puchta Alexander , Ströbel Robin , Fisel Johannes , May Marvin C. , Gönnheimer Philipp , Fleischer Jürgen , Lanza Gisela 
Published: June 2023

AbstractIncreasingly volatile markets, higher numbers of product variants and more sophisticated customer demands lead to a soaring complexity of productions themselves and their operations. An enabling technology that allows to cope with this increased complexity is digitization, as it enables data capturing and data driven analysis in production. Software-defined manufacturing (SDM) empowers to fully use the potential of digitization by decoupling physical production hardware and the associated control software. This enables an increase in the versatility of existing resources through automated generation of software for instantiations and interventions in production control. With the aim of enlarging the abstraction and decoupling capabilities of SDM, this paper presents a concept to use SDM at different abstraction levels of the production over the whole life cycle. The different abstraction levels, i.e. machine, production system and production network, are decoupled based on a service-oriented approach that defines interactions between these abstraction levels. The requirements to implement this approach are determined for the different levels where special notice is given to changing requirements over the life cycle of the production. With respect to the requirements, recommendations are given considering the integration of the concept in existing productions. Finally, potential benefits of this concept are discussed.
Asset Administration Shell for the Wiring Harness System
Authors: Schnauffer Georg , Görzig David , Kosel Christian , Diemer Johannes 
Published: June 2023

AbstractThe wiring harness is one of the most expensive components of the automobile. It is still a custom-made product despite the high-volume production in the automotive industry. However, various trends, such as increasing fragility of supply chains and functional safety demands for autonomous driving are demanding the automation of the value creation processes. But in mass customisation, automation needs the digitalisation of all information flows. Machines and processes must be able to automatically gather all required information. In the wiring harness industry, much of the information has been geared toward processing by humans and has hardly been digitised at all. One of the foundations for Industry 4.0-oriented automation is ergo the existence of complete digital information of each wiring harness. A digital twin of the wiring harness is required. The Asset Administration Shell (AAS) is the technical concept for transparently interoperating with the digital twin. The project “Asset Administration Shell for the Wiring Harness” or in German (Verwaltungsschale für den Leitungssatz - VWS4LS) creates the first complete digital description of the wiring harness based on the AAS. As a result, all actors in the value network can access complete and consistent data (“single point of truth”), enrich it, and control its application through authorisations. To achieve this goal, a consortium from all levels of the value network was assembled for VWS4LS starting with the OEM, through the suppliers at the various levels of the value chain, and to the software suppliers and machine manufacturers. This publication presents the project and its initial results.
Case Study on Localization for Robotic Wire Harness Installation
Authors: Wnuk Markus , Zürn Manuel , Paukner Matthias , Ulbrich Sascha , Lechler Armin , Verl Alexander 
Published: June 2023

AbstractWire harness installation is one of the most challenging processing steps for automation in automotive production. Wire harnesses have an infinite number of degrees of freedom, such that they change their shape continuously during manipulation. As of today, the human’s ability to perceive the wire harness and deal with its shape changes, is unmatched by any technical solution. Therefore, wire harnesses are still installed manually. This paper proposes a concept for wire harness localization and applies it for robotic wire harness installation. The concept uses two stereo cameras, one to perceive the shape of the wire harness and obtain rough position estimates of wire harness components, and a second for accurate 6D pose estimation of the individual components. The concept is evaluated with a case study on localization in a car chassis, where the accuracy and limitations of the concept are investigated by practical experiments.