A Hybrid FEM-Neural Network Approach for Predicting Ethylene Vinyl Acetate Expansion Shape on Injection Molding Process

2012 ◽  
Vol 16 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Yi-Ren Jeng ◽  
De-Shin Liu ◽  
Honz-Tzong Yau
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Vinyl Acetate ◽  
Ethylene Vinyl Acetate ◽  
Injection Molding Process ◽  
Network Approach ◽  
Molding Process ◽  
Neural Network Approach

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

scholarly journals Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Polymers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3874
Author(s):  
Yan-Mao Huang ◽  
Wen-Ren Jong ◽  
Shia-Chung Chen
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Transfer Learning ◽  
Analysis Data ◽  
Mold Temperature ◽  
Training Data ◽  
Injection Molding Process ◽  
Backpropagation Neural Network ◽  
Molding Process ◽  
Injection Speed

This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.

Melt temperature learning control of injection molding process based on CMAC neural network

2011 ◽  
Vol 31 (1) ◽  
Author(s):  
Yonggang Peng ◽  
Wei Wei
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Control Method ◽  
System Modeling ◽  
Learning Control ◽  
Injection Molding Process ◽  
Cerebellar Model Articulation Controller ◽  
Molding Process ◽  
Melt Temperature ◽  
Cmac Neural Network

Abstract Accurately monitoring and controlling melt temperature in the injection molding process can be a challenge. A barrel temperature model was achieved with the use of a system modeling method. Because of time variance, uncertainty and non-linearity of injection molding barrel temperature, a learning control method based on the use of a cerebellar model articulation controller (CMAC) neural network was proposed. Simulations and experimental results have demonstrated that this method elicits high response speed and excellent control accuracy of barrel melt temperature.

A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method

2014 ◽  
Author(s):  
Dae Seong Baek ◽  
Chengjun Li ◽  
Jung Soo Nam ◽  
Cho Rok Na ◽  
Myungho Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Nozzle Pressure ◽  
Screw Position ◽  
Diagnosis Model ◽  
Condition Diagnosis ◽  
Pressure Nozzle

The objective of this research is the development of condition diagnosis model for injection molding process based on wavelet packet decomposition (WPD), feature extraction from cavity pressure, nozzle pressure and screw position signals and probability neural network (PNN) method. The node energies from the WPD of cavity and nozzle pressure signals are identified. In addition, five (5), seven (7) and two (2) critical features are extracted from the cavity pressure, nozzle pressure and screw position signals via the new feature extraction algorithm. The node energies and critical features are input to the PNN based condition diagnosis model for the injection modeling process. A series of injection modeling experiments are conducted and their results are used to validate the model. It is demonstrated that the proposed model is applicable to diagnose the injection molding process conditions. In particular, it is also shown that the utilization of cavity pressure and screw position signals in the model can result in higher diagnosis accuracy from the case studies.

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