Software-Defined Workpiece Positioning for Resource-Optimized Machine Tool Utilization

Ströbel, Robin; Probst, Yannik; Hutt, Louisa; Fleischer, Jürgen

doi:10.36897/jme/161660

Cited by 2 publications

(6 citation statements)

References 23 publications

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“…Two datasets were used for the presented study, both consisting of recordings of machine data from a milling machine taken during the production of two distinct parts. The first dataset [34] was recorded on a DMC 60 H milling machine from DECKEL MAHO (produced by DMG MORI Aktiengesellschaft, Bielefeld, Germany), while the second [35] was recorded on a CMX 600 V milling machine from DMG MORI (produced by DMG MORI Aktiengesellschaft, Bielefeld, Germany). The recorded data included current signals from the axis and spindle motors as well as their speed and acceleration.…”

Section: Datasets and Experimentsmentioning

confidence: 99%

“…Training and validation were carried out using the dataset "Training and validation dataset of milling processes for time series prediction" [34], https://doi.org/10.5445/IR/100015 7789, last accessed on 20 January 2024, and "Training and validation dataset 2 of milling processes for time series prediction" [35], https://doi.org/10.35097/1738, last accessed on 20 January 2024.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Ströbel,

Mau,

Puchta

et al. 2024

MAKE

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This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial.

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Section: Datasets and Experimentsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Ströbel,

Mau,

Puchta

et al. 2024

MAKE

View full text Add to dashboard Cite

show abstract

“…The dataset for the time series prediction in milling processes published in [22] was used for the model development in this publication. The datasets were recorded on a DMG CMX 600 V with a Siemens Industrial Edge and a sampling rate of 500 Hz.…”

Section: Datasetsmentioning

confidence: 99%

“…As described in [23], the milling machine is assumed to be a system of rigid bodies. Due to the equation of motion, the variables acceleration a, velocity v, and process forces F process were identified as the primary influences on the energy demand p (Equations ( 1) and ( 2)) of a given block b.…”

Section: Hybrid Model For Hf Time Series Predictionmentioning

confidence: 99%

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Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Ströbel,

Probst,

Deucker

et al. 2023

Machines

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The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.

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Software-Defined Workpiece Positioning for Resource-Optimized Machine Tool Utilization

Cited by 2 publications

References 23 publications

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

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