In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases.

This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.

In today's rapidly evolving industrial landscape, efficient predictive maintenance solutions are essential for minimizing downtime and enhancing productivity. This research introduces an adaptive cloud-based model pipeline for predicting the Remaining Useful Life (RUL) of machine components, specifically ball screws. The pipeline integrates local preprocessing, edge computing, and cloud-based adaptive model training, ensuring data privacy and reducing data transmission volumes. The system classifies wear states using various machine learning mod-els and predicts RUL through regression analysis, incorporating uncertainty quantification for robust maintenance scheduling. The experimental setup includes accelerated degradation of ball screws, with data collected via a three-dimensional accelerometer. Feature extraction and data augmentation techniques are employed to enhance prediction accuracy. Random Forest and Gradient Boosting models demonstrate superior performance, with Random Forest selected for its robustness and uncertainty quantification capabilities. Empirical results indicate high prediction accuracy, with Random Forest achieving up to 91% accuracy in Phase 2. This cloud-ready predictive maintenance framework leverages scalable cloud infrastructure for efficient data processing and real-time updates, offering a practical solution for industrial applications. The proposed approach significantly advances the adoption of digital business models within the manufacturing industry, providing a reliable and efficient tool for predictive maintenance.

This approach analyses the differences between a single flank rolling test and an analytical simulation based on optical measurement data of micro gears. The focus is on a signal-based comparison of the rolling behaviour. Using methods in the time and frequency domain as well as the derivation of methods such as cross-correlation, the capability of this simulative approach is qualified. Due to short measurement times with high correlations to the test rig, an inline integration of this analytical simulation for quality assurance of micro gears in series production will be implemented as part of a holistic approach.

Anforderungen infolge des Klimawandels sowie steigende Energiekosten zwingen Unternehmen zu einem bewussteren Umgang mit Ressourcen entlang ihrer Wertschöpfungsketten. Hierdurch gewinnen Monitoring sowie Vorhersagen bezüglich des Energieverbrauchs zunehmend an Relevanz. Mithilfe von Monitoring können die Nachvollziehbarkeit erhöht und Unregelmäßigkeiten identifiziert werden, während eine Vorhersage Planungssicherheit und Möglichkeiten zur Optimierung bietet. Die vorliegende Studie analysiert bzw. bewertet den Bedarf und die aktuelle Situation in produzierenden Unternehmen.

Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model’s internal aspects, limiting applicability to white box models. To address this, Tsai, Chen and Ho introduced Black Box Adversarial Reprogramming for transfer learning with black box models. While tested primarily in image classification, this paper explores its potential in time series classification, particularly predictive maintenance. We develop an adversarial reprogramming concept tailored to black box time series classifiers. Our study focuses on predicting the Remaining Useful Life of rolling bearings. We construct a comprehensive ML pipeline, encompassing feature engineering and model fine-tuning, and compare results with traditional transfer learning. We investigate the impact of hyperparameters and training parameters on model performance, demonstrating the successful application of Black Box Adversarial Reprogramming to time series data. The method achieved a weighted F1-score of 0.77, although it exhibited significant stochastic fluctuations, with scores ranging from 0.3 to 0.77 due to randomness in gradient estimation.