Thomas Gittler


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Gittler

First Name

Thomas

ORCID

0000-0002-1932-2494

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2018 - 201962020 - 20237
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Publications 1 - 10 of 13
  • Sensors as an Enabler for Self-Optimizing Grinding Machines
    Item type: Conference Paper
    Maier, Markus; Gittler, Thomas; Weiss, Lukas; et al. (2019)
    MM Science Journal
  • Analytical user story clustering supporting requirements and synthesizing efforts for the digital transformation
    Item type: Conference Paper
    Gittler, Thomas; Lorenz, Rafael; Weiss, Lukas; et al. (2019)
    International Conference on Competitive Manufacturing (COMA 19) Proceedings. Knowledge Valorisation in the Age of Digitalization
  • Konnektivität von Maschinen, Lernen und Intelligenz
    Item type: Other Conference Item
    Wegener, Konrad; Weiss, Lukas; Mayr, Josef; et al. (2022)
  • Data acquisition and data analytics for prognostics and health management in machine tools
    Item type: Doctoral Thesis
    Gittler, Thomas (2023)
    The transformation of the manufacturing industry under Industry 4.0 shifts decision-making and optimization towards data-driven analytics. In the manufacturing industry, data-driven condition monitoring and predictive solutions for equipment, tools, and processes are becoming increasingly important, as they have proven useful in other industries such as automotive, aerospace, or marine. However, their application and penetration in machine tools and most manufacturing environments are still relatively low, due to various factors: besides the required know-how, which many machine manufacturers do not possess, diverse machine and control types, software revisions, and the lack of standardized protocols and semantics lead to a general lack of interoperability of data acquisition and analysis approaches. To meet these requirements, a versatile approach to increasing availability, performance, and quality of machine tools has been investigated, developed, implemented, and validated, taking into account technology- and industry-specific limitations and constraints. The solution consists of a part related to data acquisition structures and a part related to the analysis of the large variety of machine tool and component types. The data acquisition addresses both the use of standardized interfaces (OPC UA) to provide high-frequency measurement data, as well as the retrofitting of existing equipment or components without a control unit with data acquisition units. These acquisition units are designed to meet the data quality requirements for condition monitoring and diagnosis purposes. The data analysis part describes the processing of measurement series, depending on the operating characteristics of the components (constant, controlled-constant, and variable). For this purpose, two demand-driven analysis approaches are described in detail, which allow condition diagnoses using artificial intelligence depending on the operating characteristics of a component or a process: For processes or components with (at least partially) stationary operating conditions, an approach based on supervised machine learning has been developed. This essentially consists of an artificial neural network, which is trained on the healthy or normal state under variation of environmental influences. Subsequently, the output of the neural network is compared with the actually measured values. Deviations between the model output and the actual values provide an indication of a change in the system, which is not due to environmental influences but to wear, for example. For components or processes with variable operating conditions, an approach based on unsupervised machine learning has been developed. For comparable segments, such as quasi- stationary operating conditions or test cycles, one or more sensor signals are recorded. These time series recordings are mapped to a discrete space for comparability using so-called features. Selected features span a multi-dimensional space in which a measurement can be defined as a point defined by its feature values. The position in space thus reflects the state of the process or component in this abstract feature space. Measurements at the same states accumulate into dense areas, while changes in the state due to wear or defects move significantly away from them. By analyzing the resulting clusters, not only deviations from an original state can be detected, but also clusters of known faults or wear states can be learned, which allow the identification of a previously known type of fault or wear in addition to an obvious deviation. Both approaches are based on data-sparse measurement series collection and low-effort model creation. In addition, both approaches cover the possible operating conditions of processes and components (stationary, partially stationary, and variable), making it a holistic principle for monitoring and analyzing components.
  • Machine Tool Component Health Identification with Unsupervised Learning
    Item type: Journal Article
    Gittler, Thomas; Scholze, Stephan; Rupenyan-Vasileva, Alisa Bohos; et al. (2020)
    Journal of Materials Processing & Manufacturing Science
    Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.
  • International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning
    Item type: Journal Article
    Gittler, Thomas; Glasder, Magnus; Öztürk, Elif; et al. (2021)
    The International Journal of Advanced Manufacturing Technology
    Degraded or defect machine components and consumables negatively impact manufacturing quality and productivity. Diagnosing and predicting the wear or degradation status of critical machine components or parts are therefore of general interest. To tackle this challenge, data-driven approaches based on supervised machine learning principles have demonstrated promising results. However, supervised learning models capable of degradation identification require large quantities of data. In practice, run-to-failure data in large amounts is usually not available and expensive to obtain. To overcome this issue, this study proposes an unsupervised learning approach for degradation prognostics of machine tool components and consumables. It uses time series of multi-sensor signal data, which are transformed into a feature representation. The features consist of various characterizations of the time series, allowing to make different signal measurements comparable, and cluster them according to their feature values. The herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions. The novelty in the proposed approach lies within the identification of continuous component and part degradation states based on unsupervised learning principles. The proposal is verified and demonstrated on an exemplary data set containing a small sample of run-to-failure multi-sensor signals of milling inserts and their corresponding wear state. By the application of the proposed procedure on the exemplary data set, we demonstrate that an unsupervised clustering approach is capable of separating wear data such that meaningful and accurate estimations of the part condition are possible. The advantages are its ability to cope with scarce data sets, its limited engineering and hyperparameter tuning effort, and its straightforward implementation to a multitude of degradation and wear diagnostics scenarios.
  • Smart factory equipment integration through standardised OPC UA communication with companion specifications and equipment specific information models
    Item type: Journal Article
    Stoop, Fabian; Ely, Gerald; Menna, Robert; et al. (2019)
    International Journal of Mechatronics and Manufacturing Systems
  • Help me to help you – The role of AI in supporting manufacturing workers
    Item type: Report
    Lorenz, Rafael; Gittler, Thomas (2020)
  • Towards predictive quality management in assembly systems with low quality low quantity data – a methodological approach
    Item type: Conference Paper
    Gittler, Thomas; Relea, Eduard; Corti, Donatella; et al. (2019)
    Procedia CIRP ~ 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering
    Intervention costs in machine assembly increase rapidly with assembly progress. For early interventions, multivariate control charts or predictive quality management systems can be installed, yet they require large and high-quality datasets. In discrete manufacturing, data is limited by the quantity produced, making it cumbersome to obtain the required quantity for statistical modeling. In this study, a methodology for the setup of predictive quality management systems is presented. It demonstrates strategies for low-quality low-quantity datasets in discrete production. The boundary conditions of the assembly system, the process requirements, and the combination of physical and statistical modeling via feature engineering are highlighted.
  • Dawn of new machining concepts: Compensated, intelligent, bioinspired
    Item type: Conference Paper
    Wegener, Konrad; Gittler, Thomas; Weiss, Lukas (2018)
    Procedia CIRP ~ 8th CIRP Conference on High Performance Cutting (HPC 2018)
    The impact of Industrie 4.0 onto machine tools is significant, despite the fact, that quite some of the novelties discussed within this new paradigm have their roots decades earlier. But especially the concerted action, which strives the development of sensors, controls, data processing together with connectivity, unprecedented data integration and the notion of cyber physical production systems open up new development lines towards manufacturing systems as enablers for the progress in manufacturing. Highly developed compensation concepts are developing into state depending AI-supported strategies. Maintenance becomes predictive, as learning of machines becomes global and model based. Further inspirations taken from biological systems are adopted for machining centres and drive a biological transformation of manufacturing machines. Machine intelligence becomes the basis for executing manufacturing processes, which requires a close integration of process intelligence (CAM-systems) and machine controls.
Publications 1 - 10 of 13