Publications

@article{fernandez-robles_machine-vision-based_2017,

title = {Machine-vision-based identification of broken inserts in edge profile milling heads},

author = {Laura Fernández-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov},

url = {https://www.sciencedirect.com/science/article/pii/S0736584515300806},

year  = {2017},

date = {2017-01-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {44},

pages = {276–283},

abstract = {This paper presents a machine vision system designed to detect and identify broken inserts in edge milling head tools used for aggressive machining of thick plates. The system localizes inserts by detecting the screws, calculates the expected position and orientation of the cutting edge using geometrical operations, and compares it with the real edge to detect any deviations. The proposed method was evaluated on a new publicly available dataset, achieving a harmonic mean of precision and recall of 91.43%. The results indicate that the system is effective and ready for real-time implementation in monitoring machining tools.},

note = {Publisher: Pergamon},

keywords = {Automatic Identification, Edge Milling, machine vision, Tool Breakage},

pubstate = {published},

tppubtype = {article}

}