An Improvement of Physics Based Compressive Sensing With Domain Decomposition to Monitor Temperature in Fused Filament Fabrication Process

2019 ◽  
Author(s):  
Yanglong Lu ◽  
Yan Wang
Keyword(s):  
Domain Decomposition ◽  
Compressive Sensing ◽  
Manufacturing Systems ◽  
Matching Pursuit ◽  
Domain Decomposition Method ◽  
Mass Matrix ◽  
Fused Filament Fabrication ◽  
Basis Matrix ◽  
Decomposition Approach ◽  
Recovery Algorithm

Abstract Sensors in manufacturing play an important role in monitoring and improving the quality of products. However, the rising cost of sensing subsystems and the bandwidth limitation of data transmission are challenges in modern manufacturing systems, which rely on a large number of sensors. Recently, a physics based compressive sensing (PBCS) approach was proposed to monitor manufacturing processes with reduced number of sensors and amount of collected data. PBCS significantly improves the compression ratio from classical compressed sensing by incorporating the knowledge of physical phenomena in specific applications. In this paper, a modified orthogonal matching pursuit (OMP) recovery algorithm, called constrained OMP, is developed for PBCS when coherence exists between the measurement matrix and basis matrix. The efficiency of PBCS recovery is also improved by introducing a domain decomposition approach, which can reduce the size of model matrices, such as the conduction matrix and mass matrix in the transient heat transfer application. The improved PBCS with the domain decomposition method is used to monitor the temperature distribution in the cooling process and real-time printing process of fused filament fabrication.

Physics Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

2021 ◽  
pp. 1-30
Author(s):  
Yanglong Lu ◽  
Eduard Shevtshenko ◽  
Yan Wang
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Three Dimensional ◽  
Physical Models ◽  
Manufacturing Processes ◽  
Fused Filament Fabrication ◽  
Basis Matrix ◽  
Temperature Distributions ◽  
Digital Twins ◽  
Printing Processes

Abstract Sensors play an important role in monitoring manufacturing processes and update their digital twins. However, the data transmission bandwidth and sensor placement limitations in the physical systems may not allow us to collect the amount or the type of data that we wish. Recently, a physics based compressive sensing (PBCS) approach was proposed to monitor manufacturing processes and obtain high-fidelity information with the reduced number of sensors by incorporating physical models of processes in compressed sensing. It can recover and reconstruct complete three-dimensional temperature distributions based on some limited measurements. In this paper, a constrained orthogonal matching pursuit algorithm is developed for PBCS, where coherence exists between the measurement matrix and basis matrix. The efficiency of recovery is improved by introducing a boundary-domain reduction approach, which reduces the size of PBCS model matrices during the inverse operations. The improved PBCS method is demonstrated with the measurement of temperature distributions in the cooling and real-time printing processes of fused filament fabrication.

Image Reconstruction Based on Compressive Sensing via CoSaMP

2014 ◽  
Vol 631-632 ◽  
pp. 436-440 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Curvelet Transform ◽  
Stopping Rule ◽  
Compressive Sampling ◽  
New Method ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Number Of Iterations

Compressive Sampling Matching Pursuit (CoSaMP) is a new iterative recovery algorithm which has splendid theoretical guarantees for convergence and delivers the same guarantees as the best optimization-based approaches. In this paper, we propose a new signal recovery framework which combines CoSaMP and Curvelet transform for better performance. In classic CoSaMP, the number of iterations is fixed. We discuss a new stopping rule to halting the algorithm in this paper. In addition, the choice of several adjustable parameters in algorithm such as the number of measurements and the sparse level of the signal also will impact the performance. So we gain above parameters via a large number of experiments. According to experiments, we determine an optimum value for the parameters to use in this application. The experiments show that the new method not only has better recovery quality and higher PSNRs, but also can achieve optimization steadily and effectively.

scholarly journals A non-overlapping Schwarz domain decomposition method with high-order finite elements for flow acoustics

2020 ◽  
Vol 369 ◽  
pp. 113223
Author(s):  
Alice Lieu ◽  
Philippe Marchner ◽  
Gwénaël Gabard ◽  
Hadrien Bériot ◽  
Xavier Antoine ◽  
...  
Keyword(s):  
Finite Elements ◽  
Domain Decomposition ◽  
Decomposition Method ◽  
Domain Decomposition Method ◽  
High Order ◽  
Overlapping Schwarz

Parallel finite-element method using domain decomposition and Parareal for transient motor starting analysis

2019 ◽  
Vol 38 (5) ◽  
pp. 1507-1520 ◽  
Author(s):  
Yasuhito Takahashi ◽  
Koji Fujiwara ◽  
Takeshi Iwashita ◽  
Hiroshi Nakashima
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Parallel Computing ◽  
Domain Decomposition ◽  
Parallel Computation ◽  
Domain Decomposition Method ◽  
Space Time ◽  
Content Type ◽  
Parallel Performance ◽  
Element Method

Purpose This paper aims to propose a parallel-in-space-time finite-element method (FEM) for transient motor starting analyses. Although the domain decomposition method (DDM) is suitable for solving large-scale problems and the parallel-in-time (PinT) integration method such as Parareal and time domain parallel FEM (TDPFEM) is effective for problems with a large number of time steps, their parallel performances get saturated as the number of processes increases. To overcome the difficulty, the hybrid approach in which both the DDM and PinT integration methods are used is investigated in a highly parallel computing environment. Design/methodology/approach First, the parallel performances of the DDM, Parareal and TDPFEM were compared because the scalability of these methods in highly parallel computation has not been deeply discussed. Then, the combination of the DDM and Parareal was investigated as a parallel-in-space-time FEM. The effectiveness of the developed method was demonstrated in transient starting analyses of induction motors. Findings The combination of Parareal with the DDM can improve the parallel performance in the case where the parallel performance of the DDM, TDPFEM or Parareal is saturated in highly parallel computation. In the case where the number of unknowns is large and the number of available processes is limited, the use of DDM is the most effective from the standpoint of computational cost. Originality/value This paper newly develops the parallel-in-space-time FEM and demonstrates its effectiveness in nonlinear magnetoquasistatic field analyses of electric machines. This finding is significantly important because a new direction of parallel computing techniques and great potential for its further development are clarified.

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