TY - JOUR
T1 - Quality evaluation of honing surface groove features based on improved Level-Set analyses
AU - Dai, Jiacheng
AU - Zeng, Wenhan
AU - Lu, Wenlong
AU - Wang, Jian
AU - Shan, Mingguang
AU - Jiang, Xiangqian
N1 - Funding Information:
The work is funded by National Natural Science Foundation of China ( 51875227 , 52075206 ), Natural Science Foundation of Hubei Province of PRC ( 2019CFA038 , 2021BAA056 ), and Shenzhen Technical Project (JCYJ20190809181005690, JCYJ20210324141814038).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Honing groove features of a cylinder liner surface contain helpful information on surface performance. Traditional honing surface evaluation methods do not focus on groove features, which is significant to surface functions. Hence, a topographical feature segmentation algorithm and corresponding evaluation parameters are needed. This paper proposes an improved level-set-based segmentation algorithm for groove feature extraction. The proposed algorithm was tested on a real honing surface dataset compared to some state-of-art algorithms, including the classical height thresholding and watershed segmentation. The results show that the proposed method is robust against initial conditions and improves both segmentation accuracy and computational speed. Specifically, a 30% accuracy improvement over the best-tested level-set algorithm has been observed. Another case study on fold-mental characterization was conducted, which also validated the accuracy and speed performance of the proposed algorithm.
AB - Honing groove features of a cylinder liner surface contain helpful information on surface performance. Traditional honing surface evaluation methods do not focus on groove features, which is significant to surface functions. Hence, a topographical feature segmentation algorithm and corresponding evaluation parameters are needed. This paper proposes an improved level-set-based segmentation algorithm for groove feature extraction. The proposed algorithm was tested on a real honing surface dataset compared to some state-of-art algorithms, including the classical height thresholding and watershed segmentation. The results show that the proposed method is robust against initial conditions and improves both segmentation accuracy and computational speed. Specifically, a 30% accuracy improvement over the best-tested level-set algorithm has been observed. Another case study on fold-mental characterization was conducted, which also validated the accuracy and speed performance of the proposed algorithm.
KW - Honing groove feature
KW - Level-set function
KW - Surface topography
UR - http://www.scopus.com/inward/record.url?scp=85123853587&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2022.110789
DO - 10.1016/j.measurement.2022.110789
M3 - Article
AN - SCOPUS:85123853587
VL - 190
JO - Measurement
JF - Measurement
SN - 1536-6367
M1 - 110789
ER -