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RISS 인기검색어
- 검색키워드
- 저자 : M. Z. NUAWI (검색결과 2 건)
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Enhancing Clustering Algorithm with Initial Centroids in Tool Wear Region Recognition
Nur Adilla kasim,M. Z. Nuawi,J. A. Ghani,Muhammad Rizal,N. A. Ngatiman,C. H. C. Haron 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.5
Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. Nevertheless, the algorithm is sensitive to the initial centroids giving various solution every time the system updating. Regular unsupervised K-means is refocused as semi-supervised Fixum K-means. It is embedded with a new tactic to recapture the K value and new initial seedings computation to kick off the system until it converges. Force components of cutting force F c , thrust force F t and perpendicular cutting force F cn were extracted from Neo-MoMac cutting force measurement device. The analysis threshold represents a natural-sorted input vector as Z -rot coefficient ( R Z ) corresponds to the number of cutting accomplish a strong correlation ( R 2 = 0.8511) over wear evolution. The clustering system adopted a new calculation of initial centroids has successfully determined the three regions for only a single assignment and achieving the optimal distance squared through eight given data sets. It is conflicting with the standard K-means that return different clustering structure in each run, while K-means + + replicates several times to achieve minimum objective function. During the course, F-Km delivered robust and consistence clustering results of 85% accuracy over standard K-means and four times converges faster than K-means + + . The silhouette value average score is 0.8504 (highest score is 0.9207) of how well-distributed the resulting clusters. The clustering system has identified the tool to stop cutting at approximate VB = 0.213 mm before the tool condition enters the failure region of abnormal phase ( VB < 0.250 mm ).s The proposed system functioned effectively in clustering the data obtained from cutting tests performed within a reasonable range of wear stages. Precision and robustness analysis have proved F-km to score 100% attainment for clustering assignment output and replicability. In contrast, K-means scored 76.3% for precision and ranging from 5 to 33% for robustness. Whereas, K-means + + scored 33% for robustness and a higher chance of time complexity compared to F-km. F-Km is found to be more accurate, time savvy and robust than standard K-means and K-means + + . Therefore, the method can be reliably used for observing tool wear state recognition without training and equivocate traditional direct tool wear.
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FATIGUE FEATURES EXTRACTION OF ROAD LOAD TIMEDATA USING THE S-TRANSFORM
S. ABDULLAH,C. K. E. NIZWAN,M. F. M. YUNOH,M. Z. NUAWI,Z. M. NOPIAH 한국자동차공학회 2013 International journal of automotive technology Vol.14 No.5
This paper presents the algorithm development of a new fatigue data editing technique using S-T approach. Ingeneral, the S-transform (S-T) is a time-frequency spectral localization method which performs a multi-resolution analysis onsignal. This method represents a better time-frequency resolution especially for non-stationary signal analysis. This techniquewas developed to produce shortened fatigue data for fatigue durability testing. The S-T method was applied to detect thedamaging events contained in the fatigue signals due to high S-T spectrum location. The damaging events were extracted froman original fatigue signal to produce the shortened edited signal which has equivalent fatigue damage. Three types of road loadfatigue data were used for simulation purpose, pave track, highway and country road. In this study, an algorithm wasdeveloped, to detect the damaging events in the original fatigue signal. The algorithm can be used to extract the fatiguedamaging events and these events were combined in order to produce a new edited signal which neglect the low amplitudecycles. The edited signal consists of the majority of the original fatigue damage in the shortened signal with 15-25% timereduction. Thus, it has been suggested that this shortened signal can then be used in the laboratory fatigue testing for thepurpose of accelerated fatigue testing.
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