Self-optimizing injection molding based on iterative learning cavity pressure control

Hopmann, Christian; Abel, Dirk; Heinisch, Julian; Stemmler, Sebastian

doi:10.1007/s11740-017-0719-6

Cited by 26 publications

(7 citation statements)

References 14 publications

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“…In 2016, Nam et al [11] developed a diagnostic and error prediction model for lens injection molding based on the response surface method to extract the signal of cavity pressure during the injection molding process and measure the shape error of the lens they want to produce by embedding a pressure sensor in the mold to improve the yield of the lens in actual injection molding. In 2017, Hopmann et al [12] applied an iterative learning control method to optimize the control strategy of cavity pressure to improve the injection molding machine accuracy and further improve the injection molding product quality based on the PVT (pressure, volume and temperature) characteristics of the material, i.e., considering the relationship between pressure, volume and temperature at the same time and using the characteristic that the injection molding process is repetitive. In 2020, Stemmler et al [13] proposed the concept of model-based self-optimization in injection molding and applied a model-based parametric optimization iterative learning controller (NOILC) to monitor the variation of cavity pressure during injection molding, and the experiments of this study show that their proposed method could achieve highprecision control of cavity pressure and improve the product quality, especially the weight stability.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation

Chang

et al. 2022

Polymers

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In the process of injection molding, a certain percentage of recycled material is usually used in order to save costs. The material properties of recycled materials can change significantly compared with raw materials, and the quality of their molded products is more difficult to control. Therefore, it is crucial to propose a method that can effectively maintain the yield of the recycled material products. In addition, the variation of clamping force during the injection molding process can be determined by measuring the tie-bar elongation of the injection molding machine. Therefore, this study proposes a real-time product quality monitoring system based on the variation of clamping force during the injection molding process for the injection molding of recycled materials for plastic bottle caps. The variation of clamping force reflects the variation of cavity pressure during the injection molding process and further maps the variation of injection parameters during the injection molding process. Therefore, this study evaluates the reliability of the proposed method for three different injection parameters (residual position, metering end point and metering time). Experiments have shown that there is a strong correlation between the quality (geometric properties) and weight of the product under different molding parameters. Moreover, the three main injection parameters have a strong influence on the weight and quality of the plastic caps. The variation of the clamping force is also highly correlated with the weight of the plastic bottle cap. This demonstrates the feasibility of applying the variation of clamping force to monitor the quality of injection molded products. Furthermore, by integrating the clamping force variation index with the calibration model of the corresponding injection parameters, it is possible to control the weight of the plastic cap within the acceptable range of the product in successive production runs.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation

Chang

et al. 2022

Polymers

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“…Control signal learning control, also called iterative learning control (ILC), adjusts the controller output directly. e ILC method has been widely used in injection molding to achieve stable tracking of the time and batch sequence of the set point trajectory so as to improve process accuracy and repeatability [164,169,187,188].…”

Section: Conventional Iterative Learning Controlmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

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“…However, with advanced sensing technology, much physical information about molten polymer within the cavities can be revealed [7,8]. For example, the cavity pressure profile detected by a cavity pressure sensor can reflect the variations of molten polymer quality during the mold filling process [9,10,11,12,13]. Regarding V/P switchover control, Kazmer et al [14] compared the effects of the V/P switchover setting point on molded part quality with seven different methods, including: (1) screw position; (2) injection time; (3) machine pressure; (4) nozzle pressure; (5) sprue pressure; (6) cavity pressure; and (7) cavity temperature.…”

Section: Literature Reviewmentioning

confidence: 99%

Tie-Bar Elongation Based Filling-To-Packing Switchover Control and Prediction of Injection Molding Quality

Chen

Liu²,

Huang³

2019

Polymers

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Filling-to-packing switchover (also called V/P switchover) is critical for assuring injection molding quality. An improper V/P switchover setting may result in various defects of injection-molded parts, such as excessive residual stress, flash, short shot, and warpage, etc. To enhance a consistent molding quality, recent V/P switchover approaches adopt cavity pressure profiles requiring sensors embedded in mold cavities, which is invasive to mold cavities and more expensive. Instead of using cavity pressure sensors, by working with the most popular screw position switchover control, this study hereby proposes a novel approach of tuning V/P switchover timing using a tie-bar elongation profile. In this investigation, a dumbbell testing specimen mold is applied to verify the feasibility of the method proposed. The results show that the mold filling and packing stages can be observed along the tie-bar elongation profile, detected by mounting strain gauges on the tie bars. Also, the characteristics of the cavity pressure are similar to those of the tie-bar elongation profile under a proper clamping force condition. Moreover, the varying process parameter settings which include injection speed, V/P switchover point, and holding pressure, can be reflected in these profiles. By extracting their characteristics, the application of the V/P switchover is proved to be realistic. This research conducted an experiment to verify the proposed V/P switchover decision method based on the tie-bar elongation profile. The result showed that the fluctuation of the part’s weight corresponding to a slight change of the barrel’s temperature from 210 °C to 215 °C can be successfully controlled with this method. Besides, the maximum clamping force increment extracted from the tie-bar elongation profile was found to be a good indicator for online monitoring of the reground material variation.

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Self-optimizing injection molding based on iterative learning cavity pressure control

Cited by 26 publications

References 14 publications

Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation

Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation

Intelligent Injection Molding on Sensing, Optimization, and Control

Tie-Bar Elongation Based Filling-To-Packing Switchover Control and Prediction of Injection Molding Quality

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