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Calibration modeling and feature selection for micromagnetic material characterization using machine learning methods
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DOI：10.1515/auto-2025-0022.png)
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The industrial real-time application of the non-destructive testing method of micromagnetic material characterization offers great potential for the analysis of near-surface workpiece properties. In addition to possible reductions in time and cost through the direct integration of testing methods into the machining process, which so far has only been possible offline, this enables monitoring and targeted control of relevant workpiece properties during the ongoing process. A central challenge in the application of micromagnetic material characterization methods (MMV), also due to small reference datasets, is the development of calibration models. MMV can, for example, be applied in process data acquisition for process parameter optimization in manufacturing, without the need for complex ex-process reference measurements. In this contribution, a data-driven approach to calibration model development for micromagnetic material characterization is presented, in which Gaussian Process Regression (GPR) is combined with various feature selection methods. The proposed methodology is evaluated using an industrial hard turning process and assessed in terms of its performance compared to classical approaches such as Multiple Linear Regression (MLR).
Keyword:
micromagnetic material characterization
data-driven modeling
soft sensor
micromagnetic material characterization
Gaus-sian process regression
data-driven modeling
soft sensor
期刊
A
IF:
0.9
论文数:
67
被引数:
0
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引用论文
Surface integrity in metal machining - Part I: Fundamentals of surface characteristics and formation mechanisms金属加工中的表面完整性.第一部分: 表面特性和形成机理的基本原理
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OAAI分享收藏4.3.2 Vergleich subjektiver und automatisierter Merkmalsextraktion sowie Einsatz maschineller Lernalgorithmen zur mikromagnetischen Materialcharakterisierung.png)
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