Evidence theory-based reliability optimization for cross-scale topological structures with global stress, local displacement, and micro-manufacturing constraints

2021 ◽  
Vol 65 (1) ◽  
Author(s):  
Lei Wang ◽  
Xingyu Zhao ◽  
Zhangming Wu ◽  
Wenpin Chen
Keyword(s):  
Evidence Theory ◽  
Manufacturing Constraints ◽  
Reliability Optimization ◽  
Micro Manufacturing ◽  
Topological Structures ◽  
Local Displacement ◽  
Global Stress

Laser Beam Trajectory Generation for Micro-Manufacturing With a Two-Photon Polymerization Technique

2008 ◽  
Author(s):  
Chao-Yaug Liao ◽  
Jean-Claude Le´on ◽  
Ce´dric Masclet ◽  
Michel Bouriau ◽  
Patrice L. Baldeck ◽  
...  
Keyword(s):  
Trajectory Generation ◽  
Free Form ◽  
Manufacturing Constraints ◽  
Generation Process ◽  
Micro Manufacturing ◽  
Two Photon ◽  
Complex Shapes ◽  
Milling Machines ◽  
Free Form Surfaces ◽  
Two Photon Polymerization

Micro-manufacturing with a Two-Photon Polymerization (TPP) technique is an emerging manufacturing technique to produce small objects of tens of μm size. Combined with three axes numerically controlled equipment, it becomes possible to produce a wide diversity of product shapes. As a result, controlling this equipment while taking into account the manufacturing constraints raises the problem of trajectory generation. It is the purpose of this paper to show how this trajectory generation process can take advantage of the three numerically controlled axes to produce complex-shaped objects. Here, it is shown how the concept of layered manufacturing commonly used in rapid prototyping can be superseded by a combination of different path planning strategies, much in the same way free-form surfaces or complex shapes can be produced with classical milling machines. Through the proposed process, the boundary decomposition process applied to an object reduces to a set of sub-domains where groups of parallel trajectories are generated. The proposed trajectory generation process takes also into account the manufacturing constraints specific to TPP to produce microstructures of high quality. Examples of products illustrate the proposed approach and demonstrate its capabilities.

Micro Manufacturing with Laser Sintering A Scaled-up Process

2004 ◽  
Vol 7 (1-2) ◽  
pp. 269-278
Author(s):  
Hugo Leclerc ◽  
Kader Djioua ◽  
Nathalie Boudeau ◽  
Jean-Claude Gelin
Keyword(s):  
Laser Sintering ◽  
Micro Manufacturing

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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