The transition to highly customized, one-off production in modern manufacturing necessitates sophisticated process monitoring to reduce waste, minimise downtime, and alleviate operator burden. Computer Numerically Controlled (CNC) axes represent a fundamental component of automated manufacturing and offer a universal and accessible monitoring option through power supply data. By accurately predicting reference signals and comparing them with real-time measurements, deviations can be used for effective model-based process monitoring and anomaly detection. This study explores the efficacy of hybrid machine learning (ML) models in predicting reference signals for CNC axes using features derived from a physical model. Additionally, relevant but difficult-to-measure features such as process forces and the material removal rate (MMR) were made accessible through soft sensors. Various ML models were evaluated, including tree-based models (e.g. random forest (RF) and gradient boosting (GB)) and deep learning (DL) models (e.g. feed-forward neural networks (FNN), long short-term memory (LSTM), and transformers-based models (TF)). Feature importance analysis was performed, identifying velocity, acceleration, process forces, spindle torque, and MMR as crucial predictors that influence model performance. Key results indicate that tree-based models, specifically RF and GB, consistently delivered the highest accuracy, achieving R2 up to 0.98 for translatory axes and approximately 0.89 for the main spindle. These models demonstrated robustness, outperforming deep learning approaches, particularly when trained on smaller datasets. Although DL models improved with larger data volumes, their performance remained inferior compared to tree-based methods. The study underscores the potential of the integration of physical knowledge into hybrid ML models to enhance model-based process monitoring.
@article{IEEEAccess25_Stroebel,
title = {{Hybrid Machine Learning for CNC Process Monitoring (accepted)}},
author = {Robin Str{\"o}bel and Samuel Deucker and Hanlin Zhou and Hafez Kader and Alexander Puchta and Benjamin Noack and J{\"u}rgen Fleischer},
doi = {10.1109/ACCESS.2025.3573400},
journal = {IEEE Access},
month = may,
pages = {91875--91888},
volume = {13},
year = {2025}
}
Robin Ströbel, Hafez Kader, Louisa Hutt, Hanlin Zhou, Marcus Mau, Alexander Puchta, Benjamin Noack, Jürgen Fleischer
Intelligente Prozessüberwachung für die flexible Produktion: Integration von Physics-Informed Machine Learning und Active Learning Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 120, no. s1, pp. 224–231, Walter de Gruyter GmbH, March, 2025.
As products and their production become more personalized and variant rich, traditional approaches to process monitoring are reaching their limits. These are typically based on static data sets or recurring process patterns, which can lead to inaccurate predictions and increased false alarms in agile production environments. This article presents a concept for flexibilising the process monitoring of discrete production processes based on the combination of Physicsinformed Machine Learning (PIML) and Active Learning (AL). In agile production environments, this allows not only the detection of anomalies, but also the automatic update of the monitoring model in case of false alarms. As a result, the monitoring system remains accurate under variable production conditions, reducing the number of false alarms and contributing to improved Overall Equipment Effectiveness (OEE).
@article{ZWF25_Stroebel,
title = {{Intelligente Prozess{\"u}berwachung f{\"u}r die flexible Produktion: Integration von Physics-Informed Machine Learning und Active Learning}},
author = {Robin Str{\"o}bel and Hafez Kader and Louisa Hutt and Hanlin Zhou and Marcus Mau and Alexander Puchta and Benjamin Noack and J{\"u}rgen Fleischer},
doi = {10.1515/zwf-2024-0154},
issn = {0947-0085},
journal = {Zeitschrift für wirtschaftlichen Fabrikbetrieb},
month = mar,
number = {s1},
pages = {224--231},
publisher = {Walter de Gruyter GmbH},
volume = {120},
year = {2025}
}
Hafez Kader, Steven C. Marcrum, Milena Engelke, Niklas K. Edvall, Berthold Langguth, Birgit Mazurek, Jose Antonio Lopez Escamez, Dimitros Kikidis, Rilana Cima, Patrick Neff, Winfried Schlee, Christopher R. Cederroth, Benjamin Noack, Myra Spiliopoulou, Stefan Schoisswohl
Classifying Residual Inhibition in the Context of Tinnitus: An Interpretable Machine Learning Approach (to appear) Proceedings of the IEEE International Symposium on Computer Based Medical Systems (CBM2025), Madrid, Spain, June, 2025.
abstract
BibTeX
Residual inhibition (RI) is a phenomenon
observed in many tinnitus patients, where tinnitus
remains temporarily suppressed for a short duration—
typically less than a minute—after the cessation
of an appropriate masking stimulus. Despite decades
of clinical interest in RI, machine learning (ML)-based,
feature-driven classification approaches remain scarce.
In this study, we investigate the potential of ML
models to classify RI by developing a dedicated data
analysis pipeline. Given the heterogeneous nature of
the features—including numerical, binary, and ordinal
variables—we apply feature importance techniques tailored
for mixed-type data to ensure a comprehensive
evaluation and improve interpretability. Our results
demonstrate a clear separation between RI classes,
highlighting the relevance of specific clinical and audiological
factors in distinguishing them.
Building on this, we assess the predictive power of
RI classifications with high confidence within a supervised
learning framework to determine their relevance
for treatment outcome prediction. While our findings
confirm that RI can be effectively classified, they also
suggest that RI alone is not sufficient to serve as a
reliable predictor for treatment outcomes.
@inproceedings{CMBM25_Kader,
title = {{Classifying Residual Inhibition in the Context of Tinnitus: An Interpretable Machine Learning Approach (to appear)}},
author = {Hafez Kader and Steven C. Marcrum and Milena Engelke and Niklas K. Edvall and Berthold Langguth and Birgit Mazurek and Jose Antonio Lopez Escamez and Dimitros Kikidis and Rilana Cima and Patrick Neff and Winfried Schlee and Christopher R. Cederroth and Benjamin Noack and Myra Spiliopoulou and Stefan Schoisswohl},
booktitle = {Proceedings of the IEEE International Symposium on Computer Based Medical Systems (CBM2025)},
address = {Madrid, Spain},
month = jun,
year = {2025}
}
Hafez Kader, Robin Ströbel, Alexander Puchta, Jürgen Fleischer, Benjamin Noack, Myra Spiliopoulou
Finding Predictive Features for Energy Consumption of
CNC Machines Proceedings of the Berlin Workshop on Artificial Intelligence for Engineering Applications (AI4EA24), Berlin, Germany, November, 2024.
abstract
BibTeX
With rising energy costs and a growing emphasis on sustainable and efficient production, predicting the energy consumption of CNC machines
has become increasingly important. Accurate predictions can lead to significant energy savings, better planning, more informed decision-making, and alignment with smart manufacturing and Industry 4.0 initiatives.
Extensive research has been conducted in this area, utilizing both physical and analytical models, as well as expert knowledge from experiments. More recently, machine learning models have also been employed using a wide range of input features. In this paper, we examine the energy consumption of CNC machines by analyzing various features explored in different studies. We propose a method that ranks these features based on their predictive power, then groups the rankings to highlight a small subset of the most predictive features. Furthermore, we assess the stability of the predictive power of these features over time, allowing us to not only rank them by their predictive strength but also evaluate their long-term stability. Our findings indicate that only a few features are highly predictive, and their predictive
power remains consistent over time.
@inproceedings{AI4EA24_Kader,
title = {{Finding Predictive Features for Energy Consumption of
CNC Machines}},
author = {Hafez Kader and Robin Str{\"o}bel and Alexander Puchta and J{\"u}rgen Fleischer and Benjamin Noack and Myra Spiliopoulou},
booktitle = {Proceedings of the Berlin Workshop on Artificial Intelligence for Engineering Applications (AI4EA24)},
address = {Berlin, Germany},
month = nov,
year = {2024}
}
Hafez Kader, Robin Ströbel, Alexander Puchta, Jürgen Fleischer, Benjamin Noack, Myra Spiliopoulou
Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2024), Pilsen, Czech Republic, September, 2024.
Energy consumption is a critical factor that negatively impacts the environment. Sustainable production is essential for addressing the climate crisis, as low-emission manufacturing can both reduce costs and minimize environmental impact. Energy-efficient CNC machine tools significantly contribute to achieving ambitious environmental objectives. In recent years, numerous studies have focused on low-energy consumption production, analyzing factors that contribute to sustainable manufacturing. When using the analytical or empirical model, factors and corrections might be omitted. With advancements in machine learning and the increasing availability of large datasets, models are being developed to predict energy consumption with high accuracy. However, these models often overlook the importance of features that contribute to a transparent prediction process and their influence on the results. In our paper, an LSTM model is initially utilized to predict the energy consumption of CNC machines. Following this, a method is devised to rank the features based on their predictive power, considering temporal variations. We show that some of the features ranked in the top positions agree with independent literature findings, while others are new and demand further investigation.
@inproceedings{MFI24_Kader,
title = {{Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes}},
author = {Hafez Kader and Robin Str{\"o}bel and Alexander Puchta and J{\"u}rgen Fleischer and Benjamin Noack and Myra Spiliopoulou},
booktitle = {Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2024)},
address = {Pilsen, Czech Republic},
doi = {10.1109/MFI62651.2024.10705783},
month = sep,
year = {2024}
}
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