Viscosity Measurement in Injection Molding Using a Multivariate Sensor

2012 ◽  
Author(s):  
N. Asadizanjani ◽  
R. X. Gao ◽  
Z. Fan ◽  
D. O. Kazmer
Keyword(s):  
Injection Molding ◽  
Polymer Melt ◽  
Experimental Tests ◽  
Injection Molding Process ◽  
Online Measurement ◽  
Cavity Pressure ◽  
Molding Process ◽  
Ir Detector ◽  
Melt Temperature

Online measurement of polymer melt properties during an injection molding process is a key to provide a high quality plastic product. In-situ cavity pressure and temperature sensors are used to observe the polymer states in the mold cavity during an injecting molding process. A new multivariate sensor is introduced to measure pressure, temperature, velocity, and viscosity of polymer melt as the key parameters of the melt to improve the controlling process. This paper presents the viscosity calculating method based on melt velocity and the slope of melt pressure. The velocity is inferred using the melt temperature ramping rate; the new multivariate sensor detects melt temperature through the installed IR detector in the sensor, and the pressure is measured via the mounted piezoelectric rings. Injecting molding process of polymer melt is simulated under a range of melt velocity and temperature and the related viscosity values are inferred from simulation results and also from a set of experimental tests for a real injection molding process. Results are well matched with the expected rheological behavior of polymer.

scholarly journals Effect of micro injection molding parameters on cavity pressure and temperature assisted by Taguchi method

Mechanika ◽  
2019 ◽  
Vol 25 (4) ◽  
pp. 261-268
Author(s):  
Quan Wang ◽  
Chongying Yang ◽  
Kaihui Du ◽  
Zhenghuan Wu
Keyword(s):  
Injection Molding ◽  
Experimental Work ◽  
Complex Geometry ◽  
Mold Temperature ◽  
Mold Cavity ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure

The injection molding process is one of the most efficient processes where mass production through automation is feasible and products with complex geometry at low cost are easily attained. In this study, an experimental work is performed on the effect of injection molding parameters on the polymer pressure and temperature inside the mold cavity. Different process parameters of the injection molding are considered during the experimental work including packing pressure, packing time, injection pressure, mold temperature, and melt temperature. The cavity pressure is measured with time by using Kistler pressure sensor at different injection molding cycles. The results show the packing pressure is significant factor of affecting the maximum of diverse spline cavity pressure. The mold temperature is significant factor of affecting the maximum cavity temperature. The results obtained specify well the developing of the cavity pressure and temperature inside the mold cavity during the injection molding cycles.

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

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe 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.

Determination of Optimum Process Parameters in Gas-Assisted Injection Molding

2011 ◽  
Vol 143-144 ◽  
pp. 494-498
Author(s):  
Ke Ming Zi ◽  
Li Heng Chen
Keyword(s):  
Injection Molding ◽  
Delay Time ◽  
Optimization Design ◽  
Gas Pressure ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Melt Temperature

With finite element analysis software Moldflow, numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot, gas pressure, delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index, the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding, optimum pre-injection shot is 94%,gas pressure is 15MPa,delay time is 0.5s,melt temperature is 240 oC. This study provided a more practical approach for the gas-assisted injection molding process optimization.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

Evaluation of a Piezoelectric Energy Extraction Device for Cavity Pressure Sensing in Injection Molding

2003 ◽  
Author(s):  
Charles B. Theurer ◽  
Li Zhang ◽  
David Kazmer ◽  
Robert X. Gao
Keyword(s):  
Injection Molding ◽  
Mechanical Strain ◽  
Piezoelectric Element ◽  
Polymer Melt ◽  
High Energy ◽  
Injection Molding Process ◽  
Molding Process ◽  
Transient Model ◽  
Piezoelectric Energy ◽  
Wide Range

This paper presents the design, analysis, and validation of a self-energized piezoelectric pressure sensor that extracts energy from the pressure differential of the polymer melt during the injection molding process. To enable a self-energized sensor design, an analytical study has been conducted to establish a quantitative relationship between the polymer melt pressure and the energy that can be extracted through a piezoelectric converter. Temperature and pressure are monitored during an injection molding cycle and the performance of the piezoelectric element is evaluated with respect to a mechanically static, electrically transient model. In addition to corroboration of the proposed model, valuable statistical information about the working temperature in the prototype sensor will prove very useful in the package design of molding cavity sensors. A linear model examining the energy conversion mechanism due to interactions between the mechanical strain and the electric field developed within the piezoelectric device is established. This model is compared to the functional prototype design to evaluate the relevance of the assumptions and accuracy. The presented design enables a new generation of self-energized sensors that can be employed for the condition monitoring of a wide range of high-energy manufacturing processes.

Simulation of Ceramic Injection Molding for Zirconia Optical Ferrule

2007 ◽  
Vol 336-338 ◽  
pp. 997-1000 ◽  
Author(s):  
Mei Min Zhang ◽  
Bin Lin
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Ceramic Injection Molding ◽  
Rectangular Distribution ◽  
Simulation Results ◽  
Die Temperature ◽  
Ram Speed ◽  
Short Shot

Zirconia Ferrule is a key part for manufacturing fiber connectors. The ceramic injection molding (CIM) process of the optical ferrule was simulated with the commercial CAE software moldflow. In order to obtain the optimum results, the orthogonal method was introduced to discuss the influence of each parameter such as die temperature, melt temperature, ram speed and gate dimension with the two kinds of distribution layout system respectively. The simulation results show that the curved distribution runner system is more suitable than the rectangular distribution one in the optical ferrule molding. Moreover, the effect of gravity on the ceramic injection molding process was discussed for determining a more reasonable balanced runner system of the special designed two-plate mold with six die cavities. It was found that short shot occurred at the top of the die cavity while other five cavities were filled well in the original designed mold. And when the top die cavity’s round runner with section diameter of 4.0mm was increased to 4.17 mm, each cavity was balanced filled without short shot.

The simulation of the warpage rule of the thin-walled part of polypropylene composite based on the coupling effect of mold deformation and injection molding process

2018 ◽  
Vol 25 (3) ◽  
pp. 593-601 ◽  
Author(s):  
Jixiang Zhang ◽  
Xiaoyi Yin ◽  
Fengzhi Liu ◽  
Pan Yang
Keyword(s):  
Injection Molding ◽  
Coupling Effect ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Plastic Part ◽  
Molding Process ◽  
Melt Temperature ◽  
Packing Pressure ◽  
Thin Walled ◽  
Packing Time

Abstract Aiming at the problem that a thin-walled plastic part easily produces warpage, an orthogonal experimental method was used for multiparameter coupling analysis, with mold structure parameters and injection molding process parameters considered synthetically. The plastic part deformation under different experiment schemes was comparatively studied, and the key factors affecting the plastic part warpage were analyzed. Then the injection molding process was optimized. The results showed that the important order of the influence factors for the plastic part warpage was packing pressure, packing time, cooling plan, mold temperature, and melt temperature. Among them, packing pressure was the most significant factor. The optimal injection molding process schemes reducing the plastic part warpage were melt temperature (260°C), mold temperature (60°C), packing pressure (150 MPa), packing time (2 s), and cooling plan 3. In this situation, the forming plate flatness was better.

The Effect of Injection Molding Process Parameters on the Buckling Properties of PBT/Short Glass Fiber Composites

2011 ◽  
Vol 284-286 ◽  
pp. 550-556 ◽  
Author(s):  
Ming Hsiung Ho ◽  
Pin Ning Wang ◽  
Chin Ping Fung
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Injection Molding Process ◽  
Skin Area ◽  
Molding Process ◽  
Melt Temperature ◽  
Short Glass Fiber ◽  
Filling Time ◽  
Short Glass

This study investigates the effect of various injection molding process parameters and fiber amount on buckling properties of Polybutylene Terephthalate (PBT)/short glass fiber composite. The buckling specimens were prepared under injection molding process. These forming parameters about filling time, melt temperature and mold temperature that govern injection molding process are discussed. The buckling properties of neat PBT, 15 wt%, and 30 wt% are obtained using two ends fixed fixture and computerized closed-loop server-hydraulic material testing system. The fracture surfaces are observed by scanning electron microscopy (SEM). The global buckling forces are raised when increased the fiber weight percentage of PBT. Also, the fracture mechanisms in PBT and short glass fiber matrix are fiber pullout in skin area and fiber broken at core area. It is found that the addition of short glass fiber can significantly strengthen neat PBT.

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.

Optimization of the Injection Molding Process Parameters Based on Moldflow and Orthogonal Experiment

2013 ◽  
Vol 561 ◽  
pp. 239-243 ◽  
Author(s):  
Yong Nie ◽  
Hui Min Zhang ◽  
Jia Teng Niu
Keyword(s):  
Injection Molding ◽  
Orthogonal Experiment ◽  
Mold Temperature ◽  
Processing Parameters ◽  
Influential Factors ◽  
Injection Molding Process ◽  
Range Analysis ◽  
Molding Process ◽  
Melt Temperature ◽  
Plastic Parts

This article is using Moldflow analysis and orthogonal experimental method during the whole experiment. The injection molding process of motor cover is simulated under various technological conditions.After forming the maximum amount of warpage of plastic parts for evaluation.According to the range analysis of the comprehensive goal, the extent of the overall influence to the processing parameters, such as gate location, melt temperature, mold temperature and holding pressure is clarified.Through analyzing the diagrams of influential factors resulted from the simulation result,the optimized process parameter scheme is obtained and further verified by simulation.

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