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PublicationLife cycle analysis results for engine blisk LCA( 2024-02-05)Purpose - The aviation industry has seen consistent growth over the past few decades. To maintain its sustainability and competitiveness, it is important to have a comprehensive understanding of the environmental impacts across the entire life cycle of the industry, including materials, processes and resources; manufacturing and production; lifetime services; reuse; end-of-life; and recycling. One important component of aircraft engines, integral rotors known as Blisks, are made of high-value metallic alloys that require complex and resource-intensive manufacturing processes. The purpose of this paper is to assess the ecological and economical impacts generated through Blisk production and thereby identify significant ‘hot-spots’. Design/methodology/approach - This paper focuses on the methodology and approach for conducting a full-scale Blisk life cycle assessment (LCA) based on ISO 14040/44. Unlike previous papers in the European Aerospace Science Network series, which focused on the first two stages of LCA, this publication delves into the "life cycle impact assessment" and "interpretation" stages, providing an overview of the life cycle inventory modeling, impact category selection and presenting preliminary LCA results for the Blisk manufacturing process chain. Findings - The result shows that the milled titanium Blisk has a lower CO2 footprint than the milled nickel Blisk, which is less than half of the global warming potential (GWP) of the milled nickel Blisk. A main contributor to GWP arises from raw material production. However, no recycling scenarios were included in the analysis, which will be the topic of further investigations. Originality/value - The originality of this work lies in the detailed ecological assessment of the manufacturing for complex engine components and the derivation of hot spots as well as potential improvements in terms of eco-footprint reduction throughout the products cradle-to-gate cycle. The LCA results serve as a basis for future approaches of process chain optimisation, use of "greener" materials and individual process improvements.
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PublicationAn optimization approach for a milling dynamics simulation based on Quantum Computing( 2024-02-01)
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PublicationMachinability study in orthogonal cutting of additively manufactured Inconel 718 with specifically induced porosity( 2024-02-01)In comparison to conventional manufacturing technologies, additive manufacturing (AM) offers great design freedom, the integration of functions into components, new lightweight design concepts and high material efficiency. In aerospace and turbomachinery, this technology is increasingly coming into focus, especially the laser-based powder bed fusion of metals (PBF-LB/M) process. PBF-LB/M is already used for some aerospace components, which are often exposed to high thermal and mechanical loads. Dependent on the component geometry, support structures are required for AM, which then usually have to be removed by machining. One suitable support structure is the use of material with specifically induced porosity. This ensures good heat dissipation and thus homogeneous component properties, high retention forces and short process times in PBF-LB/M. However, the machinability of porous, additively manufactured material has hardly been researched so far. One preliminary investigation of milling porous, additively manufactured Inconel 718, though, showed significantly poorer machinability of the porous material compared to the dense material. To further examine this phenomenon, this paper presents the results of fundamental machinability studies with porous, additively manufactured Inconel 718 in orthogonal cutting. The investigations with tungsten carbide cutting tools on a special fundamental test rig include the analysis of the cutting force, the chip geometry, the chip temperature and the surface quality. The research results provide explanations for the poorer machinability of the porous material and derived approaches for improving the machinability in future studies.
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PublicationInitial experiments to regenerate the surface of plasma-facing components by wire-based laser metal deposition( 2024)
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PublicationData-driven indirect punch wear monitoring in sheet-metal stamping processes( 2024)The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating the use of indirect methods to infer its condition. Past research approaches utilized a plethora of machine learning models to infer the punch wear state from suitable process signals, but have been limited by the lack of industrial-grade process setups and sample sizes as well as their insufficient interpretability. This work seeks to address these limitations by proposing the sheared surface of the scrap web as a proxy for the punch wear and modeling its quality from acoustic emission signals. The experimental work was carried out in an industrial-grade fine blanking process setting. Evaluation of the model performances suggests that the utilized regression models are capable of modeling the relationship between acoustic emission signal features and sheared surface quality of the scrap webs. Subsequent model inference suggests adhesive wear on the punch as a root cause for the sheared surface impairment of the scrap webs. This work represents the most extensive modeling effort on indirect punch wear monitoring in sheet-metal stamping both from a model prediction and model inference perspective known to the authors.
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PublicationImproved lifetime estimation of shot-peened shaft bores using a numerical approach( 2024)
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PublicationBlisk Specific Query Language (BLISQL) - An approach for domain specific data querying in Blisk Manufacturing( 2023-07-18)Product lifecycle management (PLM) is constantly improved by a steadily growing amount of data collected along the product and process development chain. This data supports designing optimized geometries and manufacturing processes. For the optimization of the manufacturing process, data from process design and manufacturing are extracted and processed. The use of a query language is helpful to make the extraction more efficient. Query languages, referring to the specific domain of a component, simplify the formulation of the queries. We present an approach for domain specific data querying in blisk manufacturing based on the Resource Description Framework (RDF) using SPARQL.
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PublicationOn the importance of domain expertise in feature engineering for predictive product quality in production( 2023-07-18)Machine Learning (ML) offers significant potential for quality management in production with predictive analytics. Key aspects to building ML models are the selection and engineering of features from data. They allow the usage of relevant data for training ML models. Using the right features consequently improves the quality of the ML models. However, feature engineering requires knowledge of the data, data preprocessing techniques, algorithms, the domain, and use case. Hence, automatic feature engineering tools have become popular. In this paper, we investigate how domain experts and automatic tools compare for engineering features based on a time series dataset from production.
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PublicationDigital twins for cutting processes( 2023)Collecting and utilizing data in industrial production are becoming increasingly important. One promising approach to utilize data is the concept of digital twin (DT). DTs are virtual representations of physical assets, updated by real data and enhanced by models. This paper provides an overview of DTs for cutting processes. After giving a definition, we discuss requirements derived from representative use cases. As process models are central for DT creation, we present an overview of the latest research as well as conditions for how it can be implemented in industrial environments. The paper concludes with main challenges for future research.
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PublicationPotential of prediction in manufacturing process and inspection sequences for scrap reduction( 2023)With increasing quality requirements, especially for safety-critical products such as in medical technology, manufacturing companies face the challenge, that the quality inspection of component characteristics may become a crucial factor for economic production. For increasing the ratio of correctly identified scrap without increasing the amount of physical inspection, prediction models pose a valuable tool. Prediction models in manufacturing build process models through process data collected during the manufacturing process. The prediction models can for example be used to predict the product quality from the process data, which enables the substitution of physical quality inspections through virtual inspections. Before selecting suitable prediction models, checkpoints in the manufacturing process and inspection sequence (MPIS) for integrating the prediction models profitably have to be identified. Therefore, the paper aims to provide a methodology to generate integrated quality strategies for MPIS that combine physical inspection with the prediction of quality characteristics. Furthermore, a methodology for the economic evaluation of the quality strategies as decision support is provided. Since the number of prediction models available for predicting quality based on process data is manifold, the explicit recommendation for the implementation of a certain prediction model is not the focus of this paper. The methodology presented rather focuses on the identification of suitable checkpoints in the MPIS for prediction model integration. For generating quality strategies suitable checkpoints in the manufacturing process sequence have to be identified. Therefore, three aspects are considered, the influence of the manufacturing process on the change in a characteristic and the overall importance of a characteristic for the component function as well as the ability of the characteristic and process for being predicted. A prediction recommendation is calculated that enables the derivation of quality strategies as a combination of physical and virtual inspection for different characteristics in different checkpoints. For the economic evaluation of the derived quality strategies two cases for supplementing physical inspection with prediction models to either compensate for the alpha- or beta-error rate are presented. The most reasonable quality strategy is chosen according to its economic potential. For validation, the applicability of the methodology for integrating prediction models in manufacturing process and inspection sequences to a use case of medical technology is demonstrated successfully. It is shown that the methodology supports users in identifying possible quality strategies and their economic evaluation for manufacturing process and inspection sequences as a basis for the decision regarding quality strategies to be implemented.
