Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Verschleißschutz einer Schneckengeometrie durch funktional gradierte Materialien

2024-06 , Marquardt, Raphael , Osayi, Jason , Kmieciak, Siegfried , Biegler, Max , Rethmeier, Michael

Hochbelastete Stahlbauteile lassen sich durch Auftragen von Kobalt-Chrom Legierungen vor Verschleiß schützen. Die plötzliche Änderung der Materialeigenschaften führt jedoch zu Spannungen und Rissen im Anbindungsbereich. Daraus resultierende Abplatzungen stellen eine Gefahr für die Funktionsfähigkeit der Maschine und damit für Mensch und Umwelt dar. Um die Belastbarkeit der Schutzschicht zu verbessern, kann die Anbindung durch einen gradierten Materialübergang optimiert werden. Diese funktional gradierten Materialien können mittels pulverbasiertem Directed Energy Deposition aufgetragen werden. Die Methodik zum Aufbau und zur Qualitätssicherung solcher Materialien wurde in vorangegangenen Arbeiten für dickwandige Geometrien gezeigt. Für dünnwandige Geometrien ist die Anwendbarkeit bisher unzureichend untersucht worden. Diese Arbeit zeigt am Beispiel einer dünnwandigen gradierten Schneckengeometrie die Einsatzfähigkeit der Methodik. Dafür wird die Gefügestruktur der Gradierung auf Fehler untersucht und der Härteverlauf gemessen. Außerdem wird die relative Dichte anhand eines bereits trainierten neuronalen Netzes vorhergesagt und mit einer Porositätsuntersuchung verglichen.

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Dynamic error characteristics of robot motion analyzed for the suitability of visual-servoing

2024 , Radke, Marcel , Haninger, Kevin , Kröger, Ole , Krumpek, Oliver

The knowledge of the absolute positioning accuracy of a robotic arm is crucial to assess the feasibility of certain tasks. In evaluating the feasibility, a robot manipulator as well as a possibly external sensing system must be chosen. In choosing a robot arm, lightweight robots are often preferred because they require less safety precautions, but they can also be less accurate compared to a stiff industrial robot. A stiff industrial robot resists external loads better, resulting in a higher accuracy with payload or process forces, and oscillates less in motions. Additionally, typical robot inaccuracies must be considered: (i) absolute positioning errors due to kinematic model errors, (ii) error due to resonance or external forces, (iii) path-following errors from limitations in the dynamic model and control. For tasks where the goal object has an unknown or varying pose, its pose can be measured with a vision system and used to compensate the robot motion. When the measurement and compensation is done continuously, it realizes closed-loop visual servoing. This can reduce the absolute error, but only the components of the error which are of a low frequency relative to the motion control bandwidth of the robot. To evaluate whether a specific robot can meet a certain accuracy requirements with a visual servoing system, better understanding about the characteristics of the robot error is needed. For example, the frequency distribution of the robot error can indicate what proportion can be compensated with closed loop control - only that less than the position bandwidth of the robot (typically 3-7 Hz). Datasheets typically provide the accuracy value only for repeatability while the accuracy during motion and the influence of dynamic effects are ignored. If the endeffector oscillates during motion causing a positional error and at which frequency is typically not reported - leaving unanswered, if it can be compensated by control. The contribution of this paper is the experimental evaluation of an absolute accuracy during the robot motion, towards evaluating the accuracy with a visual servoing system. A tracker system is used to collect the motion data of a CNC milling machine, a Universal Robots UR5, and an industrial robot (Comau Racer7-1.4) under various motion speeds. The frequency distribution and histograms of the error are analysed with regard to possible sources and the suitability to reduction with visual servoing.

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Development of a Damped Toolholder for Vibration-Assisted High-Precision Milling Using High Damping Metals

2024 , Tschöpel, Julius , Hocke, Toni , Polte, Mitchel , Fleiner, Bernfried , Dicke, Clemens , Uhlmann, Eckart

Ultrasonic vibration-assisted machining (UVAM) is one of the most promising innovations in high precision machining of advanced materials such as titanium alloys for aerospace applications or silicon carbide materials for the semiconductor industry. UVAM improves the process efficiency by increasing tool life through lower cutting forces and temperatures. However, in vibration-assisted milling applications, it is necessary to isolate the spindle system from the vibrations induced by the oscillation actor in order to maintain the long-term accuracy of the machine. This paper presents an innovative approach based on the integration of high damping materials into the toolholder to improve its damping capacity while maintaining the necessary stiffness. Therefore, the tool holder was developed with cylindrical damping inserts in a radial pattern with shrink fit connections. According to the results it was shown that the damping capability of a conventional toolholder can be improved without unacceptably reducing its radial stiffness compared to a standard toolholder geometry.

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Implementing human-robot collaboration in highly dynamic environments: Assessment, planning and development

2024 , Bastidas Cruz, Arturo , Jaya, T. , Thiele, Gregor , Krüger, Jörg

Human-robot collaboration (HRC) applications have been slowly making their path in the industry. Although the required hardware and the methods for the planning and development of collaborative robotic applications are mostly already developed, some industrial branches still struggle to implement HRC. This is the case in motorcycle production, where, unlike car production, the assembly line has been optimized for manual work. Based on the use case described above, this paper identifies new requirements of HRC for automated screwing assembly operations in flexible production environments. In order to compensate deviations in the position of the tool relative to the workpiece, a screwing strategy based on force control is proposed. Parameter sensitivity is considered and supported experimentally with a screwing task performed by a cobot, where a method for contact detection between the nutrunner and the screw head is analyzed. This paper brings a guideline for experts from the manufacturing system engineering to implement HRC in highly dynamic assembly environments.

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AI-based welding process monitoring for quality control in large-diameter pipe manufacturing

2024-04-25 , Gook, Sergej , El-Sari, Bassel , Biegler, Max , Rethmeier, Michael

The paper presents the experimental results into the development of a multi-channel system for monitoring and quality assurance of the multi-wire submerged arc welding (SAW) process for the manufacture of large diameter pipes. Process signals such as welding current, arc voltage and the acoustic signal emitted from the weld zone are recorded and processed to provide information on the stability of the welding process. It was shown by the experiments that the acoustic pattern of the SAW process in a frequency range between 30 Hz and 2.5 kHz contains the most diagnostic information. In the spectrogram of the acoustic signal, which represents the time course of the frequency spectrum of the welding process, the formation of weld irregularities such as undercuts could be reliably identified. The on-line quality assessment of the weld seam produced is carried out in combination with methods of artificial intelligence (AI). From the results obtained, it can be concluded that the use of the latest concepts in welding and automation technology, combined with the high potential of AI, can achieve a new level of quality assurance in pipe manufacturing.

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Automated 2D-3D-Mapping and Assessment of Defects Obtained from 2D Image Detection on a 3D Model for Efficient Repair of Industrial Turbine Blades

2024 , Joost, Robert , Mönchinger, Stephan , Engeländer, Lukas , Lindow, Kai

Manufacturing processes suited to repair worn-out machine parts are increasingly gaining traction in industrial practice. This evolution is driven by the desire to contribute to sustainable production by extending the lifetime of a part, and methods and machine tools have evolved in the last years, such that the refurbishment reaches equivalent quality compared to new parts. Yet, repair processes often remain too tedious and expensive to be profitable, and are therefore not put in practice. This could be prevented by increased efficiency and automation. In order to repair parts, the defects need to be detected and assessed. This task lies in the field of Reverse Engineering (RE). In the scope of repair and overhaul, RE aims to obtain data and models that can be fed to subsequent applications, such as path planning for additive manufacturing. This paper presents a highly automated process for defect inspection by the example of industrial turbine blades. The current process requires many analogue work steps and human intervention. The developed software processes defects obtained by AI-based image classification. It mainly consists of camera scene calibration, 3D pose estimation, and 2D-3D-mapping. The gained value is the new composition of existing technologies and their customization for turbine blades. Accuracy and computational duration are assessed. The presented method is able to enhance the reparation process and can be deployed to different applications and industries.

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Comparison of Ethereum Smart Contract Analysis and Verification Methods

2024 , Happersberger, Vincent , Jäkel, Frank-Walter , Knothe, Thomas , Pignolet, Yvonne Anne , Schmid, Stefan

Ethereum allows to publish and use applications known as smart contracts on its public network. Smart contracts can be costly for users if erroneous. Various security vulnerabilities have occurred in the past and have been exploited causing the loss of billions of dollars. Therefore, it is in the developer’s interest to publish smart contracts that serve their intended purpose only. In this work, we study different approaches to verify if Ethereum smart contracts behave as intended and how to detect possible vulnerabilities. To this end, we compare and evaluate, different formal verification tools and tools to automatically detect vulnerabilities. Our empirical comparison of 140 smart contracts with known vulnerabilities shows that different tools vary in their success to identify issues with smart contracts. In general, we find that automated analysis tools often miss vulnerabilities, while formal verifiers based on model checking with Hoare-style source code annotations require high effort and knowledge to discover possible weaknesses. Specifically, some vulnerabilities (e.g., related to bad randomness) are not detected by any of the tools. Formal verifiers perform better than automated analysis tools as they detect more vulnerabilities and are more reliable. One of the automated analysis tools was able to find only three out of 16 Access Control vulnerabilities. On the contrary, formal verifiers have a hundred percent detection rate for selected tests. As a case study with a smart contract without previously known vulnerabilities and for a more in-depth evaluation, we examine a smart contract using a two-phase commit protocol mechanism which is key in many smart contract applications. We use the presented tools to analyze and verify the contract. Thereby we come across different important patterns to detect vulnerabilities e.g. with respect to re-entrancy, and how to annotate a contract to prove that intended the restriction and requirements hold at any time.

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Data augmentation for inertial sensor based human action recognition using deep learning

2024 , Kuschan, Jan , Filaretov, Hristo , Krüger, Jörg

Human Activity Recognition (HAR) approaches are predominantly based on supervised deep learning and benefit from large amounts of labeled data - an expensive resource. Data augmentation enriches labeled datasets by adding synthetic data, which is substantially cheaper, and often results in improved model performance, but is very rarely used for sensor data. This work explores data augmentation for inertial-sensor-based HAR by transforming the data through physically interpretable operations. The main studies were conducted on the Opportunity and the Overhead Car Assembly (OCA) datasets. For these experiments, only 20% of the available training data were used, and the experiments were conducted in an 8-fold cross-validation procedure over different subsets of the training data. The results show that simple geometric augmentations can be beneficial in many cases. Timewarping proved to offer the most reliable single augmentation, improving the average F1 score of Opportunity from 0.570 to 0.597 and of OCA Mixed from 0.884 to 0.906. Combining augmentations improved the accuracy in almost all scenarios but to a degree comparable to timewarping. Applying augmentations on all the available training data improved the F1 score compared to the base case with no augmentations, although this effect is more pronounced for datasets with more similar training and test data: for the OCA Mixed variant, the average F1 score improved from 0.917 to 0.933, while for the OCA Leave-One-Out (LOT) variant, the average F1 score did not significantly change. For Opportunity, which similarly to OCA LOT uses a participant-based training-test split, the F1 score improved from 0.684 to 0.697.

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Image-Based Incremental Learning for Part Recognition of Used Automotive Cores in Reverse Logistics

2024 , Briese, Clemens , Chavan, Vivek Prabhakar , Schlüter, Marian , Lehr, Jan , Kröger, Ole

This paper presents a study on image-based incremental learning for part recognition of used automotive parts, also known as cores. The use of Machine Learning (ML) in the recognition of used parts has proven to be effective in suggesting Original Equipment Number (OEN) based on images and logistics data of a core. This leads to a four-eye process where the worker and ML interact through an assistance system. In reverse logistics, the spectrum of parts handled is constantly changing, making it difficult to have a "complete" image or sensor-based data set. The study focuses on the ramp-up phase of an ML implementation project in a real-world automotive core sorting station. There are two stations equipped with sensors such as RGB cameras. The sorted parts were acquired over a period of one year. Incremental learning was employed to cope with the growing dataset and the growing number of classes to be identified without retraining a model from scratch. Open source and state-of-the-art incremental ML learning methods such as POD-Net and Foster were tested against the common joint training approach used for most benchmarks in computer vision. The best-fitting open-source method for this problem was identified as POD-Net used with a self-supervised pretrained ResNet50. For the ramp-up of an ML-based core recognition a combination of incremental learning and joint training was found to be useful. It starts learning from a small number of digitized parts (14 classes), while maintaining a high recognition accuracy rate throughout the year, with a final class count of 100 (an increase of approx. 600%), which is a subset of a real application problem. The results of this study show that the proposed method is efficient, plastic, and energy-saving. Thus, it is a promising approach for the recognition of used automotive parts.

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Concept for a modular system model for energy-efficiency monitoring of factory supply systems

2024 , Sigg, Stefan , Thiele, Gregor , Trapp, Marvin , Krüger, Jörg

Companies in the manufacturing industry are facing the challenges of both reducing energy costs as well as driving decarbonization. As a result, energy efficiency optimization of factory operations is gaining importance. Due to their high share in the energy consumption of a factory, industrial supply technology is of interest for these optimizations. Particularly cooling systems used in factories often offer considerable potential for energy efficiency improvements, some of which can be realized through optimized control strategies. Optimization based on control technology in particular requires a high level of energy consumption transparency in order to identify potentials and measure efficiency improvements. However, industrial supply systems are often complex and interconnected facilities composed of a combination of various individual assets. Consequently, the energy efficiency monitoring and analysis of such systems typically require a high manual effort. To reduce this effort, we propose the development of a modular system model which decomposes complex, interconnected energy systems to individual, recurring assets. The system model consists of a standardized data exchange format, a standardization of structural and behavioral models in the form of a model library for industrial supply systems at different hierarchy levels, and a standardized interface for using the data model on a target platform such as an energy management software. The data model of the data exchange format maps data points such as the control and media interfaces as well as energy performance indicators of the individual assets in a standardized and consistent way. Similar to the concept of digital twins, the knowledge of manufacturers and operators about the system is to be seamlessly combined and utilized. By connecting the interfaces of the individual asset models, an aggregate structural model of a factory supply system is built. The aggregate structural model enables the calculation of consistent and comparative energy performance indicators at equipment and system level. In this way, the implementation of energy efficiency monitoring and the assessment of energy efficiency potentials and improvements is facilitated. The system model concept is demonstrated using an industrial cooling system comprising individual assets such as a cooling tower, a chiller and pumps.

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