@article{CHHOR2021702,
title = {Predictive quality for hypoid gear in drive assembly},
journal = {Procedia CIRP},
volume = {104},
pages = {702-707},
year = {2021},
note = {54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
issn = {2212-8271},
doi = {https://doi.org/10.1016/j.procir.2021.11.118},
url = {https://www.sciencedirect.com/science/article/pii/S2212827121010167},
author = {Jimmy Chhor and Stefan Gerdhenrichs and Robert H. Schmitt},
keywords = {Predictive analytics, predictive quality, data-driven optimization, rear axle drive, hypoid gear},
abstract = {In rear axle drive assembly, drive bevel gear and crown gear are aligned within a narrow tolerance range and installed in the gearbox. Due to limited adaptability after installation, the ideal dimension is estimated a priori based on physical correlations and empirical correction terms. This paper follows a data-driven approach to predict the installation dimension with sufficient accuracy within a comprehensive optimization strategy in industrial application. An initial evaluation with production data logged in day-to-day operations suggests a significant improvement in accuracy compared to status quo and contributes to the dissemination of predictive analytics in industrial practice.}
}

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