Publications

@article{martinez_san_martin_segmentation_nodate,

title = {A segmentation approach for evaluating wear of inserts in milling machines with computer vision techniques},

author = {Guillermo Martínez San Martín and Laura Fernández-Robles and Enrique Alegre and Óscar García-Olalla Olivera},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=4jZgNVkAAAAJ&sortby=title&citation_for_view=4jZgNVkAAAAJ:Se3iqnhoufwC},

abstract = {Measuring tool wear in milling machines is an important task to evaluate the lifetime of the cutting parts (inserts) and deciding whether we should replace them. In our research, we propose to use computer vision algorithms to perform this task. Part of the research is to evaluate the accuracy of different segmentation algorithms that segment the area of wear. We have used two methods: k-Means and Mean Shift. To evaluate the segmentation results the Dice coefficient was used, obtaining with Mean Shift a QS= 0.5923 for all the edges and a QS= 0.6831 just for edges with high wear.},

keywords = {Computer vision, milling machines, segmentation, Tool wear},

pubstate = {published},

tppubtype = {article}

}