Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

Chen, Wen-Jong; Lin, Cai Xuan; Chen, Yan Ting; Lin, Jia Ru

doi:10.1177/1687814016641293

Cited by 8 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using wave function ψ to determine the state of particles in quantum space, the probability of a particle appearing at a certain position in space can be expressed by |ψ| 2 . If the potential well in D dimension is pb id (t) in the t-th iteration of particle i [13][14][15].…”

Section: Qpso Algorithm Modelmentioning

confidence: 99%

“…It can be seen from formula (14) that the longer the total execution time of the calculation task set is, the smaller the value of fitness f will be, and the lower the calculation efficiency of the cloud computing platform will be; on the contrary, the shorter the total execution time of the calculation task set is, the larger the value of fitness f will be, and the higher the processing efficiency of the cloud computing platform will be, which meets the processing efficiency expectation of the cloud computing platform.…”

Section: Hwqpso For Task Scheduling In Cloud Computingmentioning

confidence: 99%

See 1 more Smart Citation

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

et al. 2021

View full text Add to dashboard Cite

The computing method of the average optimal position is one of the most important factors that affect the optimization performance of the QPSO algorithm. Therefore, a particle position weight computing method based on particle fitness value grading is proposed, which is called HWQPSO (hierarchical weight QPSO). In this method, the higher the fitness value of a particle, the higher the level of the particle, and the greater the weight. Particles at different levels have different weights, while particles at the same level have the same weight. Through this method, the excellent particles have higher average optimal position weight, and at the same time, the absolute weight of a few particles is avoided, so that the algorithm can quickly and stably converge to the optimal solution, and improve the optimization ability and efficiency of the algorithm. In order to verify the effective ness of the method, five standard test functions are selected to test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the algorithms are applied to the task scheduling of the cloud computing platform. Through the test experiment and application comparison, the results show that the HWQPSO algorithm can converge to the optimal solution of the test function faster than the other three algorithms. It can also find the task scheduling scheme with the shortest time consumption and the most balanced computing resource load in the cloud platform. In the experiment, compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of the maximum task scheduling was reduced by 35%, 23% and 21% respectively.

show abstract

Section: Qpso Algorithm Modelmentioning

confidence: 99%

Section: Hwqpso For Task Scheduling In Cloud Computingmentioning

confidence: 99%

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The six factors derived from the ANOVA results were used to construct the response objective. As shown in Table 6, the significant design variables three level CCD experiment, which are coded as − 1 and + 1 and the midpoint coded as 0, was employed to determine the response surface model V ingate (Chen et al 2016).…”

Section: Appendixmentioning

confidence: 99%

Digital twin of functional gating system in 3D printed molds for sand casting using a neural network

Ktari

Mansori

2020

J Intell Manuf

View full text Add to dashboard Cite

The filling stage is a critical phenomenon in sand casting for making reliable castings. Latest research has demonstrated that for most liquid engineering alloys, the critical meniscus velocity of the melt at the ingate is in the range of 0.4-0.6 m s −1 . The work described in this research paper is to use neural network (NN) technology to propose digital twin approach for gating system design that allow to understand and model its performances faster and more reliable than traditional methods. This approach was applied in the case of sand casting of liquid aluminum alloy (EN AC-44200). The approach is based first on a digital representation of filling process to perform the melt flow simulations using a combination of the gating system design parameters, selected as a training cases from Taguchi orthogonal array (OA). The second step of the approach is the data capture of functional gating design system to train up the feed-forward back-propagation NN model. The validation of the well-trained NN model is assessed by interrogating predicted ingate velocity to it and making reliable predictions with high accuracy. The claim is that such digital twin approach is an effective solution to recognize the functional design parameters from the entire filling systems used during casting process.

show abstract

“…In addition, further studies have focused on the optimisation of the geometrical descriptors such as radii of the ingates and runner to minimise liquid metal velocity using evolutionary computations, viz. GA [21], Pareto front-based multiobjective optimisation [22] and a multiobjective culture-based quantum-behaved particle swarm optimisation (PSO) [23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

Ktari

Mansori

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper reports an intelligent approach for modeling and optimisation of filling system design (FSD) in the case of sand casting process of aluminium alloy. In order to achieve this purpose, physics-based process modeling using finite element method (FEM) has been integrated with artificial neural networks (ANN) and genetic algorithm (GA) soft computing techniques. A threedimensional FE model of the studied process has been developed and validated, using experimental literature data, to predict two melt flow behaviour (MFB) indexes named ingate velocity and jet high. Two feed-forward back-propagation ANN-based process models were developed and optimised to establish the relationship between the FSD input parameters and each studied MFB index. Both ANN models were trained, tested and tuned by using database generated from FE computations. It was found that both ANN models could independently predict, with a high accuracy, the values of the ingate velocity and the jet high for training and test data. The developed ANN models were coupled with an evolutionary GA to select the optimal FSD for each one. The validity of the found solutions was tested by comparing ANN-GA prediction with FE computation for both studied MFB indexes. It was found that error between predicted and simulated values does not exceed 5.61% and 6.31% respectively for the ingate velocity and the jet high, which proves that the proposed approach is reliable and robust for FSD optimisation.

show abstract

Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

Cited by 8 publications

References 29 publications

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

Digital twin of functional gating system in 3D printed molds for sand casting using a neural network

Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

Contact Info

Product

Resources

About