Multi-cavity pressure control in the filling and packing stages of the injection molding process

1997 ◽  
Vol 37 (11) ◽  
pp. 1865-1879 ◽  
Author(s):  
David Kazmer ◽  
Philip Barkan
Keyword(s):  
Injection Molding ◽  
Pressure Control ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process

scholarly journals Injection Molding Process Control of Servo–Hydraulic System

2019 ◽  
Vol 10 (1) ◽  
pp. 71
Author(s):  
Chun-Ying Lin ◽  
Fang-Cheng Shen ◽  
Kuo-Tsai Wu ◽  
Huei-Huang Lee ◽  
Sheng-Jye Hwang
Keyword(s):  
Injection Molding ◽  
Hydraulic System ◽  
Pid Controller ◽  
Position Control ◽  
Performance Standards ◽  
Pressure Control ◽  
Injection Molding Process ◽  
Proportional Integral Derivative ◽  
Molding Machine ◽  
Molding Process

The present study constructs a servo–hydraulic system to simulate the filling and packing processes of an injection molding machine. Experiments are performed to evaluate the velocity and position control of the system in the filling stage and the pressure control in the packing stage. The results demonstrate that the proposed system meets the required performance standards when operated with the proportional-integral–derivative (PID) controller under a sampling frequency of 1000 Hz.

In-Mold Monitoring of Temperature and Cavity Pressure During the Injection Molding Process

2014 ◽  
Author(s):  
Catalin Fetecau ◽  
Felicia Stan ◽  
Laurentiu I. Sandu
Keyword(s):  
Injection Molding ◽  
Pressure Sensors ◽  
Temperature Sensors ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Shear Rates ◽  
Wide Range ◽  
Simulation Results ◽  
Two Temperature

This paper focuses on the in-mold monitoring of temperature and cavity pressure. The melt contact temperature and the cavity pressure along the flow path were directly measured using two pressure sensors and two temperature sensors fitted into the cavity of a spiral mold. Three melt temperatures and dies of different heights (1.0, 1.5 and 2 mm) were used to achieve a wide range of practically relevant shear rates. In order to analyze the extent to which the numerical simulation can predict the behavior of the molten polymer during the injection molding process, molding experiments were simulated using the Moldflow software and the simulation results were compared with the experimental data under the same injection molding conditions.

scholarly journals Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model

Polymers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 3297
Author(s):  
Jinsu Gim ◽  
Byungohk Rhee
Keyword(s):  
Injection Molding ◽  
Pressure Profile ◽  
Polymer Materials ◽  
Quality Analysis ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Part Quality ◽  
Analysis Methodology ◽  
Molding Condition

The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.

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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