M. L. Altmann, S. Bosse, C. Werner, R. Fechte-Heinen, and A. Toenjes, Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem, Materials, vol. 15, no. 20, p. 7090, Oct. 2022, doi: 10.3390/ma15207090. [Online].
Available: http://dx.doi.org/10.3390/ma15207090 Paper PDFPublisher
In this Article, the targeted adjustment of the relative density of laser additive manufactured components made of AlSi10Mg is considered. The interest in demand-oriented process parameters is steadily increasing. Thus, shorter process times and lower unit costs can be achieved with decreasing component densities. Especially when hot isostatic pressing is considered as a post-processing step. In order to be able to generate process parameters automatically, a model hypothesis is learned via artificial neural networks (ANN) for a density range from 70% to almost 100%, based on a synthetic dataset with equally distributed process parameters and a statistical test series with 256 full factorial combined instances. This allows the achievable relative density to be predicted from given process parameters. Based on the best model, a database approach and supervised training of concatenated ANNs are developed to solve the inverse parameter prediction problem for a target density. In this way, it is possible to generate a parameter prediction model for the high-dimensional result space through constraints that are shown with synthetic test data sets. The presented concatenated ANN model is able to reproduce the origin distribution. The relative density of synthetic data can be predicted with an R2-value of 0.98. The mean build rate can be increased by 12% with the formulation of a hint during the backward model training. The application of the experimental data shows increased fuzziness related to the big data gaps and a small number of instances. For practical use, this algorithm could be trained on increased data sets and can be expanded by properties such as surface quality, residual stress, or mechanical strength. With knowledge of the necessary (mechanical) properties of the components, the model can be used to generate appropriate process parameters. This way, the processing time and the amount of scrap parts can be reduced.
Chirag Shah, Stefan Bosse, and Axel von Hehl. Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics, Materials 15 (MDPI), no. 13 (2022): 4645.
Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.
(2022) Paper PDFARXIV
S. Bosse, PSciLab: An Unified Distributed and Parallel Software Framework for Data Analysis, Simulation and Machine Learning—Design Practice, Software Architecture, and User Experience , Appl. Sci. 2022, 12(6), 2887;
10.3390/app12062887 Paper PDFPublisher
Short-time and short-range device-to-device and device-to-service communication in ad-hoc mobile networks is a challenge. A prominent example of such a mobile device is the smartphone carried by users with a typical speed of 1m/s. Most Internet-of-Things (IoT) devices and smart sensors are connected via the Internet. Although, Internet access is widely available, there are places that are not covered by wireless IP networks, IP networks are not suitable for ad-hoc short-time and short-range communication, and the spatial context is not (accurately) considered by Internet connectivity. In this work, devices communicate connectionless and ad-hoc by using low-energy Bluetooth broadcast messaging available in any smartphone and in most embedded computers. Bi-directional connectionless communication is established via parallel usage of the advertisement and scanning modes by exchanging data tuples. The communication is performed via a tuple space service on each node. Tuple space access is performed by simple event-based agents. Mobile devices can act as tuple carriers that can carry tuples between different locations. Additionally, UDP-based Intranet communication can be used to access tuple spaces on a larger spatial range. The Bluetooth Low Energy Tuple Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device communication with a spatial context.
Christoph Polle, Stefan Bosse, Michael Koerdt, Björn Maack,and Axel S. Herrmann, Fast Temperature-Compensated Methodfor Damage Detection and Structural Health Monitoring with Guided Ultrasonic Waves and Embedded Systems, Proc. of the SysInt Conference, Sep. 6, 2022 to Sep. 8, 2022 - Genova, Italy, DOI: 10.1007/978-3-031-16281-7_35
(2022) Paper PDFConference
Chirag Shah, Stefan Bosse, Carolin Zinn, and Axel von Hehl, Optimization of Non-destructive Damage Detection of HiddenDamages in Fiber Metal Laminates Using X-ray Tomography andMachine Learning Algorithms, Proc. of the SysInt Conference, Sep. 6, 2022 to Sep. 8, 2022 - Genova, Italy, DOI: 10.1007/978-3-031-16281-7_37
(2022) Paper PDFConference
Stefan Bosse, Wireless Agent-based Distributed Sensor Tuple Spaces using Bluetooth and IP Broadcasting, Proc. of the 17th CONFERENCE ON COMPUTER SCIENCE AND INTELLIGENCE SYSTEMS FedCSIS 2022, Sofia, Bulgaria, 4-7 September
Stefan Bosse, BeeTS: Smart Distributed Sensor Tuple Spaces combined with Agents using Bluetooth and IP Broadcasting, CoRR abs/2204.02464
Most Internet-of-Things (IoT) devices and smart sensors are connected via the Internet using IP communication driectly accessed by a server that collect sensor information periodically or event-based. Although, Internet access is widely available, there are places that are not covered and WLAN and mobile cell communication requires a descent amount of power not always available. Finally, the spatial context (the environment in which the sensor or devices is situated) is not considered (or lost) by Internet connectivity. In this work, smart devices communicate connectionless and ad-hoc by using low-energy Bluetooth broadcasting available in any smartphone and in most embedded computers, e.g., the Raspberry PI devices. Bi-directional connectionless communication is established via the advertisements and scanning modes. The communication nodes can exchange data via functional tuples using a tuple space service on each node. Tuple space access is performed by simple evenat-based agents. Mobile devices act as tuple carriers that can carry tuples between different locations. Additionally, UDP-based Intranet communication can be used to access tuple spaces on a wider spatial range. The Bluetooth Low Energy Tuple Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device communication with a spatial context.
S. Bosse, L. Dahlhaus, U. Engel, Web data mining: collecting textual data from web pages using R,
in: Handbook of computational social science ; Volume 2: Data science, statistical modelling, and machine learning methods, Editors U. Engel et al., 2022, Routledge
ISBN 9781032111391 DOI Chapter PDFPublisher
S. Bosse, Large-scale agent-based simulation and crowd sensing with mobile agents, in: Handbook of computational social science ; Volume 2: Data science, statistical modelling, and machine learning methods, Editors U. Engel et al., 2022, Routledge
ISBN 9781032111391 DOI Chapter PDFPublisher