Publications

@article{gonzalez-laguna_design_2015,

title = {Design of a TCM system based on vibration signal for metal turning processes},

author = {Adalberto González-Laguna and Joaquín Barreiro and A Fernández-Abia and Enrique Alegre and Víctor González-Castro},

url = {https://www.sciencedirect.com/science/article/pii/S1877705815044239},

year  = {2015},

date = {2015-01-01},

journal = {Procedia engineering},

volume = {132},

pages = {405–412},

abstract = {This paper investigates the identification of cutting tool wear in steel dry turning operations using vibration signal analysis. By analyzing RMS value evolution and FFT frequency spectra, an on-line tool condition monitoring system was developed to determine when the tool condition becomes unacceptable, affecting machining quality. The study concludes that both RMS values and specific frequency amplitude ranges are correlated with tool wear.},

note = {Publisher: No longer published by Elsevier},

keywords = {Monitoring, TCM, Tool wear, Turning, Vibration},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_tool_2015,

title = {Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers},

author = {Óscar García-Olalla and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},

url = {https://www.sciencedirect.com/science/article/pii/S187770581504494X},

year  = {2015},

date = {2015-01-01},

journal = {Procedia engineering},

volume = {132},

pages = {950–957},

abstract = {This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).},

note = {Publisher: No longer published by Elsevier},

keywords = {LBP, LOSIB, Monitoring, TCM, texture description, Tool wear},

pubstate = {published},

tppubtype = {article}

}