Výsledky vyhledávání - "Robin Ströbel"

Akademický článek

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

Autor: Robin Ströbel, Marcus Mau, Alexander Puchta, Jürgen Fleischer

Publikováno v: Machine Learning and Knowledge Extraction, Vol 6, Iss 2, Pp 1072-1086 (2024)

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 sampli

Externí odkaz: https://doaj.org/article/3be8dc8253e2429496cc99169865fa2e

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Akademický článek

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability

Autor: Alexander Bott, Simon Anderlik, Robin Ströbel, Jürgen Fleischer, Andreas Worthmann

Publikováno v: Machines, Vol 12, Iss 3, p 153 (2024)

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 tar

Externí odkaz: https://doaj.org/article/e4f67142cf3d4885bb5d9711965153fc

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Akademický článek

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

Autor: Robin Ströbel, Yannik Probst, Samuel Deucker, Jürgen Fleischer

Publikováno v: Machines, Vol 11, Iss 11, p 1015 (2023)

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 need

Externí odkaz: https://doaj.org/article/b98c51be73404632b49f7068a8e59c5d

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Akademický článek

Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis

Autor: Robin Ströbel, Alexander Bott, Andreas Wortmann, Jürgen Fleischer

Publikováno v: Machines, Vol 11, Iss 11, p 1032 (2023)

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 sta

Externí odkaz: https://doaj.org/article/1eea8168b2bc4cd0be60e141848a37ca

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