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This paper presents a hybrid optimization method for optimizing the process parameters during plastic injection molding (PIM).
In our study, an optimal gradient composite film has been deposited on the NiTi alloys by optimizing the process parameters for implantation and deposition.
The established models are used for optimizing the process parameters using genetic algorithm (GA).
It should be possible to reduce or eliminate edge chipping by optimizing the process parameters based on efficient theoretical modeling of the size of the edge chip.
Grey Relational Analysis (GRA) is used for optimizing the process parameters for the material removal rate (MRR) and radial overcut (ROC).
Design of experiments (DOE) is a powerful statistical approach which is used for optimizing the process parameters through two stage esterification process, relating acid and alkaline as catalyst.
Similar(32)
The proposed approach integrates Taguchi's parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the process parameters.
A multi-objective optimization model is established to optimize the process parameters during PIM on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm.
Therefore, response surface optimization could be successfully used to optimize the process parameters and evaluate the performance of R. arrhizus as a biosorbent and to achieve an overall 49.04 % increase in biosorption.
Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals.
In order to achieve the optimal operating conditions, the CCD design was used to optimize the process parameters, and the maximum furfural yield of 80.5% was obtained.
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