Publications

@article{morala-arguello_reliability_2012,

title = {Reliability of Monitoring Signals for Estimation of Surface Roughness in Metallic Turned Parts},

author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre and María Teresa García-Ordás and Óscar García-Olalla Olivera and Daniel González-Madruga},

url = {https://www.scientific.net/AMR.498.213},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {Advanced Materials Research},

volume = {498},

pages = {213–218},

abstract = {Modern machining processes aim to enhance productivity, reliability, and cost efficiency. In this context, signal monitoring systems play a crucial role in surface roughness inspection. This study evaluates different signal types, including cutting forces and vibrations, to predict surface roughness indirectly. The findings indicate that combining force measurements with cutting conditions yields the most accurate roughness predictions. The absolute error remained below 1.28 µm using the median as a descriptor and below 1.11 µm with root mean square (RMS), making these effective approaches for roughness evaluation.},

note = {Publisher: Trans Tech Publications Ltd},

keywords = {Cutting Force Analysis, machining processes, Signal Monitoring, Surface Roughness Prediction},

pubstate = {published},

tppubtype = {article}

}