Viscosity Measurement in Injection Molding Using a Multivariate Sensor

Asadizanjani, Navid; Gao, Robert X.; Fan, Zhaoyan; Kazmer, David O.

doi:10.1115/isfa2012-7246

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Kruppa et al presented a feedback control method for calculating the viscosity of the molten polymer based on the detected nozzle pressure and temperature [31]. Asadizanjani and Gordon developed a multivariate sensor for controlling the melt quality based on pressure, temperature, and velocity measurements of the molten polymer [32,33]. Montgomery and Gallo found that the change of the cavity pressure during the cavity filling stage (ûP/ût) is proportional to the melt viscosity and hence provides a feasible means of predicting the part quality [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Chen

Tseng²,

Huang³

2019

Advances in Polymer Technology

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Quality control is a crucial issue in the injection molding process with target of obtaining a high yield rate and reducing production cost. Consequently, effective methods for monitoring the injection conditions (e.g., pressure, velocity, and temperature) in real-time and adjusting these conditions adaptively as required to ensure a consistent part quality are essential. This study proposes a quality index based on the clamping force increment during the injection cycle, as determined by four strain gauges attached to the tie bars of the injection molding machine. Also, various quality indexes for online quality monitoring and prediction purposes based on the pressure, viscosity, and energy features extracted from the pressure profiles obtained at the load cell, nozzle, and molding cavity, respectively, are compared. The feasibility of the proposed quality indexes is investigated experimentally for various settings of the barrel temperature, back pressure, and rotational speed of the plasticizing screw. It is shown that all of the quality indexes are correlated with the injection-molded quality and hence provide a feasible basis for the realization of an on-line quality monitoring and control system. Particularly, the tie-bar elongation quality index requires no modification or invasion of the injection molding system or cavity and hence provides a particularly attractive solution for monitoring and controlling the part quality.

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Section: Literature Reviewmentioning

confidence: 99%

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Chen

Tseng²,

Huang³

2019

Advances in Polymer Technology

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“…Therefore, viscosity can be determined as the ratio of nozzle pressure to injection rate, and if the flow rate is assumed to be constant, the only variable will be nozzle pressure [84]. Asadizanjani et al presented an online viscosity monitoring method based on the measured velocity and pressure of the melt [85]. Melt velocity can be calculated with the use of the ramping rate of melt temperature and the time derivative of melt pressure.…”

Section: Other In-mold Sensorsmentioning

confidence: 99%

“…Schematic representation of an optical fiber sensor for in-mold shrinkage measurement (reproduced with the permission of John Wiley and Sons [85]).…”

Section: Figurementioning

confidence: 99%

In-Mold Sensors for Injection Molding: On the Way to Industry 4.0

Ageyeva

Horvath

Kovács

2019

Sensors

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The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques—direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.

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“…The primary reason for this is that the flow behavior of polymer melts in a mold cavity is not adequately understood. Because of advancements in sensing technology, pressure and temperature sensors are available for detecting changes in the properties of polymer melts in molds and for monitoring and controlling the injection molding process [ 3 , 4 , 5 , 6 , 7 , 8 ]. Although the cost of installing cavity sensors in production systems is higher than the cost of using machine profiles, the use of cavity profiles helps to reduce prediction errors [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning

Ke¹,

Huang²

2021

Polymers

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Conventional methods for assessing the quality of components mass produced using injection molding are expensive and time-consuming or involve imprecise statistical process control parameters. A suitable alternative would be to employ machine learning to classify the quality of parts by using quality indices and quality grading. In this study, we used a multilayer perceptron (MLP) neural network along with a few quality indices to accurately predict the quality of “qualified” and “unqualified” geometric shapes of a finished product. These quality indices, which exhibited a strong correlation with part quality, were extracted from pressure curves and input into the MLP model for learning and prediction. By filtering outliers from the input data and converting the measured quality into quality grades used as output data, we increased the prediction accuracy of the MLP model and classified the quality of finished parts into various quality levels. The MLP model may misjudge datapoints in the “to-be-confirmed” area, which is located between the “qualified” and “unqualified” areas. We classified the “to-be-confirmed” area, and only the quality of products in this area were evaluated further, which reduced the cost of quality control considerably. An integrated circuit tray was manufactured to experimentally demonstrate the feasibility of the proposed method.

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Viscosity Measurement in Injection Molding Using a Multivariate Sensor

Cited by 9 publications

References 13 publications

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Quality Indexes Design for Online Monitoring Polymer Injection Molding

In-Mold Sensors for Injection Molding: On the Way to Industry 4.0

Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning

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