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Missouri University of Science and Technology
Mechanical and Aerospace Engineering Faculty Research & Creative Works
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Full-Text Articles in Engineering
Effect Of Architecture And Porosity On Mechanical Properties Of Borate Glass Scaffolds Made By Selective Laser Sintering, Krishna C. R. Kolan, Ming-Chuan Leu, Greg Hilmas, Taylor Comte
Effect Of Architecture And Porosity On Mechanical Properties Of Borate Glass Scaffolds Made By Selective Laser Sintering, Krishna C. R. Kolan, Ming-Chuan Leu, Greg Hilmas, Taylor Comte
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The porosity and architecture of bone scaffolds, intended for use in bone repair or replacement, are two of the most important parameters in the field of bone tissue engineering. The two parameters not only affect the mechanical properties of the scaffolds but also aid in determining the amount of bone regeneration after implantation. Scaffolds with five different architectures and four porosity levels were fabricated using borate bioactive glass (13-93B3) using the selective laser sintering (SLS) process. The pore size of the scaffolds varied from 400 to 1300 μm. The compressive strength of the scaffolds varied from 1.7 to 15.5 MPa …
Use Of Hopfield Neural Networks In Optimal Guidance, S. N. Balakrishnan, James Edward Steck
Use Of Hopfield Neural Networks In Optimal Guidance, S. N. Balakrishnan, James Edward Steck
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A Hopfield neural network architecture is developed to solve the optimal control problem for homing missile guidance. A linear quadratic optimal control problem is formulated in the form of an efficient parallel computing device known as a Hopfield neural network. Convergence of the Hopfield network is analyzed from a theoretical perspective, showing that the network, as a dynamical system approaches a unique fixed point which is the solution to the optimal control problem at any instant during the missile pursuit. Several target-intercept scenarios are provided to demonstrate the use of the recurrent feedback neural net formulation.