Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts Using Forward and Reverse Mapping of Artificial Neural Networks

Manjunath, Patel G.C.; Krishna, Pravin

doi:10.4028/www.scientific.net/amr.463-464.674

Cited by 21 publications

(11 citation statements)

References 8 publications

(12 reference statements)

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“…Many more researchers proposed ANN-based models of the injection molding process for a subsequent optimization of the product warpage [17][18][19][20][21][22], mechanical properties [23][24][25], or even a combination of several quality parameters together in a single model [26][27][28]. Each of the above described research works refer to an explicitly generated database, introducing an iterative data generation process and therefore costs into the optimization.…”

Section: Artificial Neural Network In Injection Moldingmentioning

confidence: 99%

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Lockner

Hopmann

2021

Int J Adv Manuf Technol

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The necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

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Section: Artificial Neural Network In Injection Moldingmentioning

confidence: 99%

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Lockner

Hopmann

2021

Int J Adv Manuf Technol

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“…In this paper, the root mean square error ( RMSE ) and the Pearson Linear Correlation Coefficient ( PLCC ) are used as the comparative evaluation indexes of the model prediction quality and the multi-model fusion quality characteristics (MFQCs),and a multi-model fusion neural network are proposed. Based on a large number of experiments, the BP neural network (BP), the support vector machine (SVM) and Classification and Regression Trees (CART) [6], which are commonly used in cement industry prediction model modelling, are selected and fused into an algorithm. Next, the fused algorithm is used in the prediction model of the cement grate cooler pressure in the cement production cooling process, which provides the parameter target for realizing the automatic control of the grate cooler.…”

Section: Volume XX 2017mentioning

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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“…Majunath and Krishna [9] use an Artificial Neural Network (ANN) to find the optimum parameter and the predicted error level for the Acrylonitrile Butadiene Styrene (ABS) material. The ANN used in this study to map the nonlinear input and output for various relationship or responses.…”

Section: A Comparison From the Previous Studymentioning

confidence: 99%

“…Not just that, the product produce are mostly variety in complex shape [6], thin, small and light. Focusing on the product quality, this process is gaining more and more attention because of the promising standard measure which focusing on the material used [7], part designing and parameters setting [8][9][10] in influencing the process so that it can be controlled. Despite that, there are some shortcoming occur alongside the process especially in maintaining the quality of the parts and cost of the products.…”

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confidence: 99%

Overview of the Injection Moulding Optimisation for Shrinkage Defect

et al. 2018

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The injection moulding process in manufacturing plastic material has become a worldwide used for more than 100 years. The used can be seen everywhere instead of became a daily used product. The complexity of the process makes it difficult to handle but the big production makes it possible to happen. The entire process not just involve the difficult cycling process, but the involvement of the materials which pertaining many types of characteristic which considered to. Every big production need a quality production product with suitable cost and time efficiency. Eventhough, alongside the process, many defects can be detected by the physically seen marks. Many people especially from the expert’s field are trying to minimise as much as they can to reduce the defects so that the residual cost can be minimise. One of the way to overcome the problems is by simulation before the real product is being produce. The simulation is made by two classes of types which classical and non-classical method. Either both of the method gave good result but it still not reaches a perfect solution. In this study, the optimisation method of non-classical and classical are used to minimise the shrinkage defects at the thick plate part.

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Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts Using Forward and Reverse Mapping of Artificial Neural Networks

Cited by 21 publications

References 8 publications

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

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

Overview of the Injection Moulding Optimisation for Shrinkage Defect

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