Identification of milling inserts in situ based on a versatile machine vision system. (October 2017)

• Record Type: Journal Article
• Authors: Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai; Castejón-Limas, Manuel
• Is Part Of: Journal of manufacturing systems. Volume 45(2017)
• Page Start: 48
• Page End: 57
• Publication Date: 2017-10
• Subjects: Machine vision -- Automatic inspection -- Milling -- Insert localization; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2017.08.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Automatic method to localize inserts in situ in a milling tool with a high number of inserts. Crucial for avoiding the manual extraction of the insert for tool wear evaluation. The method is domain independent and can be automatically configured in a new machine. Verified our method on an experimental dataset that we made available to the public. Abstract: This paper proposes a novel method for in situ localization of multiple inserts by means of machine vision techniques, a challenging issue in the field of tool wear monitoring. Most existing research works focus on evaluating the wear of isolated inserts after been manually extracted from the head tool. The method proposed solves this issue of paramount importance, as it frees the operator from continuously monitoring the machining process and allows the machine to continue operating without extracting the milling head for wear evaluation. We use trainable COSFIRE filters without requiring any manual intervention. This trainable approach is more versatile and generic than previous works on the topic, as it is not based on, and does not require, any domain knowledge. This allows an automatic application of the method to new machines without the need of specific knowledge on machine vision. We use an experimental dataset that we published to test the effectiveness of the method. We achieved very good performance with an F 1 score of 0.9674, in the identification of multiple milling head inserts. The proposed approach can be considered as a general framework for the localization and identification of machining pieces from images taken from mechanical monitoring systems.