Modeling of Pressure Die Casting Process: An Artificial Intelligence Approach

Kittur, Jayant K.; Patel, G C Manjunath; Parappagoudar, Mahesh B.

doi:10.1007/s40962-015-0001-7

Cited by 23 publications

(10 citation statements)

References 15 publications

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“…100] generations, population size, [0.75-0.9] crossover probability and [0.05-0.2] mutation probability. Similar ranges have been used in the literature[38][39][40]. Parameter values of 50 generations with population size of 25 and crossover and mutation probability of 0.8 and 0.1 respectively are found to give accurate estimates as shown in table 3.…”

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confidence: 60%

Optimization of solidification in die casting using numerical simulations and machine learning

Shahane

Aluru

Ferreira

et al. 2020

Journal of Manufacturing Processes

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In this paper, we demonstrate the combination of machine learning and three dimensional numerical simulations for multi-objective optimization of low pressure die casting. The cooling of molten metal inside the mold is achieved typically by passing water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to determine the temperature-time history and correlate the inputs to outputs. The objective of this research is to develop and demonstrate a procedure to obtain the initial and wall temperatures so as to optimize the product quality. The nondominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization in this work. The number of function evaluations required for 1 Corresponding Author

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confidence: 60%

Optimization of solidification in die casting using numerical simulations and machine learning

Shahane

Aluru

Ferreira

et al. 2020

Journal of Manufacturing Processes

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“…After selecting the structure of the fusion model, this paper verified the prediction ability of the fusion model. The specific steps are as follows [16]…”

Section: B Experimental Designmentioning

confidence: 99%

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

2020

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The reasonable control of the grate cooler is the key factor to ensure the heat exchange and cement clinker quality during the clinker cooling process. In this paper, the cement grate cooler pressure of the grate cooler is taken as the research object and a cement grate cooler pressure prediction model is proposed based on the analysis of the current status of the automatic control of the grate cooler. This model uses a multi-model fusion neural network algorithm that combines a BP neural network, a support vector machine and classification and regression trees with a neural network structure. Furthermore, the multimodel fusion quality characteristics are proposed, and the root mean square error and Pearson linear correlation coefficient of the multi-model fusion quality characteristics are used as the evaluation indicators for the prediction results of the multi-model fusion neural network. After the analysis of the cooling process of the cement clinker, we select seven input variables, and then complete the data preprocessing and model parameter selection. Finally, we predict the cement grate cooler pressure using a multi-model fusion neural network, a BP neural network, a support vector machine and classification and regression trees with three training sets to test sets ratios. Through the comparison of the root mean square error and the Pearson linear correlation coefficient evaluation indicators and their change trends, as well as the display and analysis of the final modelling results, it is found that the multi-model fusion neural network algorithm can greatly improve the accuracy of the prediction of the grate pressure, and at the same time it has good practicality for the accurate prediction of the cement grate cooler pressure in the industry.

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“…(14), (17) and (18), whose intersection sets λ 2 min . Such a lower bound depends on both the machine limits (v LIM , a LIM , ω p , ξ p ) and the cycle parameters (h, T, x SW , v 1 ).…”

Section: Inclusion Of the Constraintsmentioning

confidence: 99%

“…Some examples are neural networks [17], high-order non-linear response surfaces [18] and multivariable linear regression based on Taguchi's method [19]. Alternatively, simulation-based approaches relying on extensive numerical simulations and CAE tools are also adopted [20,21], by taking advantage of the ever-growing computational power of simulation environments.…”

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confidence: 99%

Improving the quality of die castings through optimal plunger motion planning: analytical computation and experimental validation

Fiorese

Richiedei

Bonollo

2016

Int J Adv Manuf Technol

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High-pressure die casting is a process widely used to manufacture components with high productivity and dimensional accuracy. Its main disadvantage is the high percentage of scraps, due to a high amount of defects. Thus, the identification of the parameters affecting quality of castings is the current challenge towards an efficient and effective production. In their previous work, the authors have found and statistically validated a novel parameter explaining and forecasting both static mechanical properties and porosity of castings. Such a parameter, defined as the root mean square value of the plunger acceleration in the fast shot stage, represents a measure of the average force transmitted by the plunger to the melt and has been proved to be very effective in predicting casting quality. In order to provide a practical tool for the use of this parameter, this work proposes an analytical method for its computation, starting from the plunger displacement curve or just some notable points. The method formulation takes advantage of the analytical development of the typical motion primitives adopted and also accounts for limitations due to the machine. Hence, the optimization of the process can be achieved by selecting in advance the most suitable plunger motion profile, among those feasible, that allows to improve the casting quality. Besides the theoretical formulation, a meaningful experimental validation is provided to demonstrate the correctness of the proposed parameter and of the analytical approach, as well as its ease of implementation that makes it suitable for industrial applications.

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Modeling of Pressure Die Casting Process: An Artificial Intelligence Approach

Cited by 23 publications

References 15 publications

Optimization of solidification in die casting using numerical simulations and machine learning

Optimization of solidification in die casting using numerical simulations and machine learning

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

Improving the quality of die castings through optimal plunger motion planning: analytical computation and experimental validation

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