Browsing by Subject "Particle Swarm Optimization"

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Layer-to-Layer Height Control for Laser Metal Deposition
(2008-09-10) Tang, Lie; Ruan, Jianzhoung; Sparks, Todd E.; Landers, Robert G.; Liou, Frank
A Laser Metal Deposition (LMD) height controller design methodology is presented in this paper. The height controller utilizes the Particle Swarm Optimization (PSO) algorithm to estimate model parameters between layers using measured temperature and track height profiles. The process model parameters for the next layer are then predicted using Exponentially Weighted Moving Average (EWMA). Using the predicted model, the powder flow rate reference profile, which will produce the desired layer height reference, is then generated using Iterative Learning Control (ILC). The model parameter estimation capability is tested using a four-layer deposition. The results demonstrate the simulation based upon estimated process parameters matches the experimental results quite well. Simulation study also shows that the methodology described above works well in producing the reference layer height.
Study on the Influence of Rapid Prototyping Parameters on Product Quality in 3D Printing
(2008-09-10) Arumaikkannu, G.; Anilkumar, N.; Saravanan, R.
This work aims at evaluating the relative significance of the input parameters on the product quality produced by Z510 3DP rapid fabricator. Taguchi’s L18 orthogonal array was employed to investigate the possible process parameters including binder setting saturation value (shell & core), layer thickness, and build orientation. Using a surface profilometer and CMM, a series of measurements in evaluating the 3DP products surface finish and dimensional accuracy has been carried out. Nonlinear regression model was developed and a statistical analysis was done to determine the significant factors affecting the product quality of the fabricated products. Optimal process parameter settings were proposed using Particle Swarm Optimization technique. The results were validated by conducting the confirmatory experiments.
Towards Real-time Fully Coherent Search for Gravitational Wave Signals from Compact Binary Coalescences
(2019) Mohanty, Soumya; Normadin, Marc
While a fully coherent all-sky search is known to be optimal for detecting gravitational wave signals from the inspiral and merger of a binary system of compact objects -- a compact binary coalescence (CBC) -- its high computational cost has limited current searches to less sensitive coincidence-based schemes. We have developed a code (BINARIES) that uses Particle Swarm Optimization (PSO) and an optimized numerical implementation of the mathematical formalism to speed up fully coherent all-sky search to the point where real-time analysis becomes a possibility.