Research on global coupling analysis and approximate decoupling of quality characteristics in the early design stage

2020 ◽  
pp. 095440622093484
Author(s):  
Yifan Chen ◽  
Yan Ran ◽  
Zhichao Wang ◽  
Genbao Zhang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Indicator System ◽  
Quality Characteristics ◽  
Propagation Model ◽  
Numerical Control ◽  
Design Stage ◽  
Coupling Analysis ◽  
Variation Propagation ◽  
System Solution

Due to the existence of coupling relationships, quality characteristics form a variation propagation process, which increases the risk possibility of product quality. In order to improve the robustness of quality characteristics, this paper proposes a fuzzy clustering-based key quality characteristics decoupling planning considering risk criticality. Firstly, based on the design structure matrix, a modular correlation matrix of key quality characteristics was established to quantify the coupling relationships among them. Secondly, according to the variation characteristics of quality characteristics, the variation propagation model is constructed to identify the potential quality risk. Thirdly, the fuzzy clustering algorithm is used to obtain the optimal control sequence of key quality characteristics. Finally, the computerized numerical control machine tool is taken as an actual case, the effectiveness and superiority of this method are verified by the comparison of the numerical result and method. This method provides a new indicator system solution for the coupling analysis of product design.

Calculation of Angular Variation of Spacecraft Assembly Considering Measurement Uncertainty

2013 ◽  
Vol 457-458 ◽  
pp. 1058-1063
Author(s):  
Jing Zhao Yang ◽  
Guo Xi Li ◽  
Bao Zhong Wu
Keyword(s):  
Measurement Uncertainty ◽  
Propagation Model ◽  
Design Stage ◽  
Assembly Model ◽  
Angular Variation ◽  
Early Design Stage ◽  
Assembly Accuracy ◽  
Key Characteristics ◽  
Variation Propagation ◽  
Product Assembly

The present studies of assembly accuracy predicting for complex products are mainly oriented to the early design stage. The final product assembly accuracy is computed using a variation propagation model, which is constructed based on design tolerance model and assembly model. Generally, the solving results are quite different with the actual values. In this paper, the axis angular variation calculation of the spacecraft cabin at the assembly phase was studied. And a novel analysis method for assembly variation was proposed with the consideration of measurement uncertainty of the key characteristics. An example was studied to illustrate and demonstrate the feasibility of the proposed method.

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

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