NUMERICAL INVESTIGATION OF THE POLYMER MELT FLOW IN INJECTION MOLDING BY USING ILU PRECONDITIONED GMRES

The implementation of a modern preconditioned Newton‐Krylov solvers to the polymer melt flow in injection molding is the main focus of this paper. The viscoelastic and non‐isothermal characteristics of the transient polymer flow is simulated numerically and the highly non‐linear problem solved. This non‐linear behavior results from the combination of the dominant convective terms and the dependence of the polymer viscosity to the changing temperature and the shear rate. The governing non‐Newtonian fluid flow and energy equations with appropriate approximations are discretized by finite differencing. Elliptic Grid Generation technique is used to map physical domain to computational domain. The resulting non‐linear system is solved by using Newton's method. GMRES, one of the Krylov subspace methods, used as an iterative algorithm in order to solve the linear system at each non‐linear step. Incomplete LU preconditioner is used for better convergence. Numerical solution of polymer flow is presented to demonstrate that these methods are efficient and robust for solving such flow problems.

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An Intelligent Optimization Controller for a Non-Linear System: Application to an Injection Molding Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.10-12.884 ◽

2007 ◽

Vol 10-12 ◽

pp. 884-888 ◽

Cited By ~ 1

Author(s):

J.M. Liang

Keyword(s):

Linear System ◽

Injection Molding ◽

Complex Dynamics ◽

Parameter Tuning ◽

Injection Molding Process ◽

Molding Process ◽

Intelligent Optimization ◽

Non Linear ◽

Line Process ◽

Non Linear System

The injection molding process has been well-known non-linear complex dynamics and the approach extensively applied manual control and rely on experienced engineers. An intelligent optimization controller has been designed with two series neural networks and the multi-losses function has been proven can automatically adjust the machine setting overcome the complex dynamics to upgrade part’s quality and reduce experienced engineers. The proposed method has shown promising future for expediting the on-line process parameter tuning work to other complicate non-linear system in the future.

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Multi-objective evolutionary framework for non-linear system identification: A comprehensive investigation

Neurocomputing ◽

10.1016/j.neucom.2019.12.095 ◽

2020 ◽

Vol 386 ◽

pp. 257-280 ◽

Cited By ~ 2

Author(s):

Faizal Hafiz ◽

Akshya Swain ◽

Eduardo Mendes

Keyword(s):

System Identification ◽

Linear System ◽

Comprehensive Investigation ◽

Multi Objective ◽

Non Linear ◽

Linear System Identification ◽

Non Linear System

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Application of He’s homotopy perturbation method for non-linear system of second-order boundary value problems

Nonlinear Analysis Real World Applications ◽

10.1016/j.nonrwa.2008.02.032 ◽

2009 ◽

Vol 10 (3) ◽

pp. 1912-1922 ◽

Cited By ~ 86

Author(s):

Abbas Saadatmandi ◽

Mehdi Dehghan ◽

Ali Eftekhari

Keyword(s):

Linear System ◽

Perturbation Method ◽

Boundary Value Problems ◽

Homotopy Perturbation Method ◽

Boundary Value ◽

Second Order ◽

Homotopy Perturbation ◽

Non Linear ◽

Non Linear System

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A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0020 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Akshaykumar Naregalkar ◽

Subbulekshmi Durairaj

Keyword(s):

Linear System ◽

Regulatory Control ◽

Hammerstein Model ◽

Support Vector ◽

Model Structure ◽

Linear Dynamics ◽

Vector Machines ◽

Non Linear ◽

Laguerre Filters ◽

Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

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