AbstractTaleb coined the term “antifragility” to describe systems that benefit from stressors and volatility. While nature provides several examples of systems with antifragile behavior, manufacturing has so far only aimed to avoid or absorb stressors and volatility. This article surveys existing examples of antifragile system behavior in biology, biotechnology, software engineering, risk management, and manufacturing. From these examples, components of antifragile systems and principles to implement these components are derived and organized in a framework. The framework intends to serve as guidance for practitioners as well as starting point for future research on the design of antifragile systems in manufacturing.

Monitoring systems in sheet metal forming cannot rely on direct measurements of the physical condition of interest because the space between the die component and the material is inaccessible. Therefore, in order to gain further insight into the forming or stamping process, sensors must be used to detect auxiliary quantities such as acoustic emission and force that relate to the physical quantities of interest. While it is known that changes in force data are related to physical parameters of the process material, lubricant used, and geometry, the changes in data over large stroke series and their relationship to wear are the subject of this paper. Previously, force data from different wear conditions (artificially introduced into the system and not occurring in an industry-like environment) were used as input for clustering and classifying high and low wear force data. This paper contributes to fill the current research gap by isolating structural properties of data as indicators of wear growth to quantify the wear evolution during ongoing production in industry-like scenarios. The selected methods represent either established methods in sheet metal forming force data analysis, dimensionality reduction for local structure separation or generic feature extraction. The study is conducted on a set of four experiments with each containing about 3000 strokes.

ZusammenfassungEs kann künftig möglich sein, im Internet der Dinge (engl. Internet of Things, kurz IoT) Daten und Resilienz-Services ebenso souverän wie selbstsicher zu handhaben und auszutauschen. Ähnlich wie dies heutzutage mit physischen Ressourcen möglich ist. Um diese Vision zu realisieren, entwickeln Forschende im Projekt SPAICER einen GAIA-X konformen IoT-Datenraum, der Datenproduzierende, Datenkonsumierende und Datenverarbeitende an einem digitalen Ort vereint und eine medienbruchfreie Datenökonomie ermöglicht. Mit Hilfe dieser medienbruchfreien Datenökonomie lassen sich wichtige Antworten auf relevante Fragen, wie zum Beispiel nach industrieller Nachhaltigkeit und resilienter Produktion, auf Knopfdruck ermitteln. Dabei ist der Vorteil eines IoT-Datenraums, dass skalierbare und grenzkostenfreie digitale Vermögenswerte entwickelt werden können, die Datenproduzierende von produktbezogenen zu nutzungsbezogenen Technologieführern wandeln. Hierdurch werden IoT-Daten zu Wirtschaftsgütern und IoT-Services zu digitalen Geschäftsmodellen.

The process setup of manufacturing processes is generally knowledge-based and carried out once for a material batch. Industry experts observe fluctuations in product quality and tool life, albeit the process setup remains unchanged. These fluctuations are mainly attributed to fluctuations in material parameters. An in-situ detection of changes in material parameters would enable manufacturers to adapt process parameters like forces or lubrication before turbulences like unexpectedly high tool wear or degradation in product quality occurs. This contribution shows the applicability of a deep learning time series classification architecture that does not rely on handcrafted feature engineering for the classification of hardness fluctuations in a sheet-metal coil using magnetic Barkhausen noise emission. This methodology is not limited to the detection of hardness fluctuations in sheet-metal coils and can potentially be applied for the in-situ material property classification in different manufacturing processes and for different material parameters.

Industry 4.0 is characterized by the transformation and networking of production systems as a result of utilizing digital technologies and processing huge amounts of data. The features of Distributed Ledger Technologies have the potential to revolutionize production by creating trust and transparency in stored data, eliminating dependence on single entities and performing automated transactions in real time. Due to the disruptive change coming with an implementation, manufacturers remain skeptical about the technology. To raise awareness of the potential of Distributed Ledger Technology, this paper describes valid use cases and examines the interactions with business models in real world manufacturing scenarios.

The vision of the Paris Climate Convention is global greenhouse gas neutrality by 2050. Manufacturing industry accounts for approximately 25% of global greenhouse gas. The achievement of emission targets requires joint implementation and the participation of the employee. An incentive concept based on a Distributed Ledger Technology allows the automatically reward of climate protection measures based on tamper-proof stored Key Performance Indicators. The technology also enables the automated payment of a CO2-tax proportional to energy consumption. A technology recommendation for the incentive concept based on an analysis of the technological requirements for a distributed ledger completes the paper.

The ongoing digitization of production enables the collection of increasing volumes of data. These, in turn, allow for data-driven analysis that has the potential for deepening the process understanding by discovering previously unknown connections between process components and parameters. With these opportunities, however, come substantial challenges as current industrial settings are inadequately equipped for handling these large amounts of data. While setting up a local processing infrastructure is challenging, the limited bandwidth within many shop floors as well as their network access also make an upload of all data to external compute capacities infeasible. What is needed are local, process-aware filters that allow for significant data reduction while retaining data of value that can be used for the subsequent analysis. In this paper, we thus propose to leverage In-Network Computing to dynamically detect different states of the physical processes and then filter the sensor values on the data path. Our presented architecture maps the state detection to the switch-local controlplane while fast filtering decisions are performed at line-rate in the dataplane, thus enabling flexible and quick adjustments of the chosen sensor filtering. At the example of a fine-blanking line, we consequently demonstrate that In-Network Computing can sensibly support previously infeasible data analysis techniques in the industrial production landscape.

The amount of information contained in process signals such as acoustic emission and force signals has proven vital for the detection of changes in physical conditions or quality feature prediction in sheet metal forming applications. Both signal types have also been researched in the context of wear detection, yet systems that reliably identify the wear state at a given time in sheet metal forming processes based on these signals do not exist. This paper proposes an architecture to assess the wear increase within a given time frame in an experiment based on an autoencoder. The ability of autoencoders to encode and decode signals has been widely studied and this approach leverages the fact that autoencoders are more likely to learn representative encodings on stable and homogeneous signals than on heterogeneous signals with high fluctuations. This approach utilizes the circumstance that high tool wear leads to changes in the signal and signal fluctuation. In consequence, autoencoders can be utilized to track tool wear progression without the need for labelled data. The findings show a strong similarity to physical models for the wear progression of tool components, indicating the validity of this approach. Additionally, an analysis of the signals yields characteristic effects of the considered force signals that could specifically represent wear resistance.

Tool wear during fine blanking impairs the quality of the sheared part, which is assessed in regular samples in an industrial environment. This leads to scrap production and low planning reliability due to low wear predictability. A tool condition monitoring based on acoustic emission (AE) data for the prediction of the remaining useful life of the tool would mitigate those effects. In a production series, AE signals were recorded, and the tool wear observed. The AE signals were then preprocessed using feature engineering and visualized using linear and nonlinear dimensionality reduction techniques. These visualizations preserve information about the data structure even in two dimensions and resemble the temporal dependent observed tool wear during fine blanking.

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Machine hammer peening (MHP) has the potential to introduce residual compressive stresses and strain hardening into the surface area as well as to generate a defined surface structure and thus improve the wear behavior of spur gears. However, MHP has not yet been used for machining tooth flanks. The aim of this work is to investigate the interactions of process parameters and process forces as well as the modification of the surface integrity due to MHP. Therefore case-hardened 16MnCr5 analogy specimens were peened, examined and compared with the initial condition and interpreted. The results showed an increase in residual compressive stresses and strain hardening in the surface area compared to specimens in the initial condition. The surface hardness was increased by 12 %. The residual compressive stresses were increased to a depth of tb = 0.41 mm.

Decentralized systems can provide privacy, security, and immutability without relying on a central authority. In the context of the Internet of Things, Distributed Ledger Technologies (DLT) can be important facilitators for implementing IoT economies. IOTA is a public DLT, which was created to provide support for the Internet of Things. The IOTA protocol enables high transaction throughput at zero cost, while also being highly scalable. These characteristics make it possible to collect sensor data with transparency and security, using a distributed public network. Despite the great potential of IOTA as a distributed protocol for sensor data, there are still no studies that demonstrate how the connection between sensors and IOTA can occur, and what the characteristics and limitations of the system are. We began exploratory technological research which includes the modeling of a system that allows the integration of IOTA with sensors, using IOTA as the data layer. An implementation of the model has been developed, based on data collection from the Bosch XDK110 multisensor and a storage/visualization application. Because the IOTA protocol requires Proof of Work to send and broadcast data over the network, which takes a few seconds, it is not possible to have granular live data from sensors directly interacting with the DLT. However, we will study the case of an application that allows for this by buffering data and inserting it to the IOTA network at a rate that nodes can handle.