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Item type:Publication, Access status: without files , Sensorvergleich zur ML-Verschleiß-prognose beim Drehen : Bewertung der Informationsqualität verschiedener Sensoren zur Vorhersage des Werkzeugverschleißes beim Längsdrehen unter Einsatz von Maschinellem Lernen(2026-05-25); ; Suitable sensors are required for data-driven wear predictions. The objective was to quantify the predictive power of five sensors for predicting the wear mark width VBmax using deep learning models for the turning process. The models based on the cutting force components achieved the highest prediction accuracy (MAE = 9.68 µm, R<sup>2</sup> = 0.93). Acoustic emission, temperature, and tool vibrations were in a close mid-range with MAE ~ 15 µm. The workpiece surface exhibited the lowest information content with an MAE of 23.47 µm.Publicationtype: Journal Article53 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The Enabled Worker Combining Smart Hand Tools, Active Exoskeletons and Digital Assistance Systems in the Production of the Future(De Gruyter, 2025-06-21); ; ; ; Manual work processes require high quality, process reliability and ergonomics. The increasing individualization of products further increases these requirements. Smart hand tools, (active) exoskeletons and digital assistance systems can help here. Linking these technologies in terms of sensors, information technology and mechanics can enable a context detection and adaptive process support in order to improve quality, increase process reliability and reduce stress on workers.Publicationtype: Journal Article69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Surface quality during grinding of CFRPSurface Quality during Grinding of CFRP. The use of carbon fibre-reinforced polymers in high-performance electric motors enables higher levels of efficiency and system rigidity. The demands on the surface quality of the individual components are so high that they can only be achieved by grinding processes. This paper investigates the influence of fibre orientation and abrasive grain size on the surface roughness during side-face grinding of CFRP.Publicationtype: Journal Article34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Cutting carbon footprint with smart tool management and cemented carbide (WC-Co) upcycling(Institutionelles Repositorium der Leibniz Universität Hannover, 2024-07); ; ; During the production of a small to medium-sized aircraft, approximately 250,000 rivet holes are required. These must be manufactured according to strict aviation specific technical, quality, and safety standards. High-quality cemented carbide (WC-Co) drilling tools are used to ensure the necessary precision and durability. However, the high energy requirement for production, especially for sintering the WC-Co blanks, and grinding the final tool geometry negatively impacts the carbon footprint of aircraft production. This study aims to analyse the environmental impact of each step in the WC-Co tool production chain and proposes innovative methods to upcycle worn tools without the need of conventional recycling processes. To reduce the climate impact, a novel smart digital tool management system is introduced containing digital twins of the individual tools. This system is complemented with three new methods of upcycling WC-Co drilling tools: regrinding worn tools to smaller diameters, applying a PVD-coating to compensate for diameter deviations, and de- and recoating diamond coatings on WC-Co tools. Preliminary results in modelling the climate impact show that these strategies can reduce the carbon footprint by up to 53 %.Publicationtype: Conference Paper67 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Enhancing Tool Replacement Decisions in Milling of Ti-6Al-4V Using Convolutional Neural Networks and Time-series-to-image Encoding(2023-12-11); ; ; ; In manufacturing of structural components for the aerospace industry, the application of milling tools with fixed service life time is a common practice. A tool replacement is often based on predetermined schedules rather than the actual wear. Consequently, tools may be replaced too soon or too late with respect to a defined final state of wear. To address this issue, different evaluation methods based on sensor data from the machining process have been proposed in the literature as a means of predicting tool wear and automating tool change decisions. However, decision models often do not take into account the variability of the data due to a change in the cutting parameters and tool load, thereby limiting their applicability to specific situations. In this paper, a convolutional neural network (CNN) is trained based on the pretrained GoogLeNet architecture to detect different states of tool wear in circumferential milling of Ti-6Al-4V with varying cutting parameters. Experiments have been carried out to capture sensor data, such as structure-borne noise, machine drive data, and cutting forces, in comparable states of tool wear under varying cutting parameters, for the purpose of model training and assessment. Different types of sensor data and time-series-to-image encoding techniques have been investigated in terms of model performance. The conducted experiments successfully applied gramian angular fields (GAF), markov transition fields (MTF), recurrence plots (RP), wavelet transform (WT) and short time fourier transform (STFT) to different signal regions (entry, full cut and exit). Using a trained CNN for classification, accurate states of tool wear can be predicted based on the created images. WT performed best in the investigations. Furthermore, an investigation was conducted to determine the level of deviation in actual tool wear on a sample of 10 tools that were removed from an active production line after being used for a fixed duration of 60 minutes. The level of deviation on tool wear across the 10 tools was found to be significantly high, indicating that the tools were not wearing out uniformly and that there may be a need for better tool monitoring and maintenance practices. This ML-based approach enhanced the tool replacement decisions by predicting similar states closer to the actual wear of the tools. Thus, the use of a CNN is found to be a helpful method to predict tool wear even under variable process conditions. They are able to provide more accurate and timely tool replacement decisions, reducing the risk of premature or delayed replacement, and improving overall manufacturing efficiency.Publicationtype: Conference Paper55 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Improving Efficiency and Sustainability Through Predictive Tool Wear Monitoring During Manual Drilling of CFRP(2023-11-18); ; ; ; Aircraft assembly involves many high-precision rivet holes, with around one-third drilled manually by experienced workers. Wireless electrical machines are increasingly replacing pneumatic drilling machines to reduce energy consumption. Nevertheless, the lifespan of the cutting tools varies between 20 and 70 drill holes in carbon fiber reinforced polymer (CFRP) depending on the individual experience and constitution of the worker, resulting in a large waste in cemented carbide. This article proposes a method that uses the internal sensors of the drilling machine to predict tool wear during manual drilling. The main challenge is the rapid wear of the tools and the unknown feed rate, which depends on the individual workers’ constitution and experience. By predicting tool wear, this method can help reduce waste and improve the efficiency, sustainability, and precision of manual drilling operations in aircraft structural assembly.Publicationtype: Conference Paper69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Characterization of the surface error during peripheral milling of thin-walled, near-net-shaped structures(Springer, 2023-11-18); ; ; ; Lightweight aerospace structural components are usually milled out of plate-material, converting up to 95% of the material into difficult to recycle chips. Therefore, additive-manufacturing holds great resource saving potential through the near-net-shape production of thin-walled parts. However, these parts pose new challenges. For example, previous methods such as the “waterline”-path approach, which uses the residual stiffness of the solid-block to reduce the deformation of the thin-walls due to process forces, can only be applied to a very limited extent. This results in a greater deflection of the near-net-shape, additively-manufactured structure. In this paper the surface error is investigated using milling experiments. An analytical approach to qualitatively predict the surface characteristics is provided. The results get used to discuss whether an adjustment to the tool path is suitable to compensate for the dimensional error. Such a compensation method has the potential to efficiently machine near-net-shape semi-finished parts within the required tolerances.Publicationtype: Conference Paper58 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , ML Models for Intelligent Tool Replacement(2023-11); ; The wear limits of milling tools should not be exceeded with regard to the workpiece quality. Current approaches of relying on fixed service lifetimes for tool replacement overlook the crucial factor of varying wear rates and fail to determine the optimal times for replacement. This article presents an approach that uses ML algorithms in the decision-making process, which enables a tool replacement while considering varying wear rates within a defined range of cutting parameters. © 2023 Walter de Gruyter GmbH, Berlin/Boston, GermanyPublicationtype: Journal Article38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dimensional Error Compensation during Finish MillingThere is a limit to productivity when finish milling thin-walled aerospace structural components. Larger engagement conditions lead to deformation of the workpiece, resulting in a permanent dimensional error that does not meet tight tolerance requirements. In this work, sensor technology is used to record the workpiece deformation in the process and derive tool paths that compensate for the dimensional error.Publicationtype: Journal Article42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 53