Tool Wear Assessment Approach Based on the Neighborhood Rough Set Model and Nearest Neighbor Model

• Published in: Shock and vibration Vol. 2020; no. 2020; pp. 1 - 15
• Main Authors: Sheng, Ren, Zhu, Xiaoran
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited; Wiley
• Subjects: Algorithms; Approximation; Artificial intelligence; Composite materials; Neighborhoods; Neural networks; Physics; Research facilities; Rough set models; Spectrum analysis; Tool wear
• ISSN: 1070-9622; 1875-9203
• DOI: 10.1155/2020/8876187

In order to assess the degree of wear of tool for milling process quantitatively, a new assessment approach is proposed. Firstly, making full use of the neighbor information, two sensitive features are selected by using the neighborhood rough set model, and then, boundary curves are established by using the nearest neighbor model with noncounter data in two dimension spaces. Secondly, the intersection area or expanding area is used to describe the difference between two boundary models because the intersection area or expanding area can consider the effect of distance and angle simultaneously in two dimension spaces. Thirdly, after determining a baseline state, a new quantitative assessment indicator (QAI) can be calculated based on the intersection area or expanding area. The QAI can directly measure the difference between the model of baseline state and the model of unknown state and indirectly measure the degree of wear of tool. Finally, the effectiveness of the assessment approach is proven by using the Milling Dataset which was provided by the NASA Ames Research Center.