On-line measurement of clamping force for injection molding machine using ultrasonic technology

Zhao, Yao; Zhao, Peng; Zhang, Jianfeng; Huang, Junye; Xia, Neng; Fu, Jianzhong

doi:10.1016/j.ultras.2018.08.013

Cited by 46 publications

(20 citation statements)

References 37 publications

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“…Huang et al confirmed that a proper setting of the clamping force is essential to enhance the quality and consistency of injection molded parts [39]. Zhao et al developed a methodology based on ultrasonic technology to in situ detect clamping force [40,41], and the advantages are simple, practical, and nondestructive. In summary, current injection molding quality estimation methods generally exploit the signals obtained from pressure sensors located in or near the molding cavity.…”

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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“…Local mechanical properties are also a vital factor in the application of micro parts, such as micro gears . Current testing methods include ultrasonic technology, dielectric sensing, magnetic levitation, and nanoindentation . For example, Zhao et al .…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13][14][15] Local mechanical properties are also a vital factor in the application of micro parts, such as micro gears. 16 Current testing methods include ultrasonic technology, [17][18][19][20][21] dielectric sensing, 22,23 magnetic levitation, [24][25][26][27][28][29][30] and nanoindentation. 31,32 For example, Zhao et al first found the linear relationship between the degree of crystallinity and the ultrasound velocity.…”

Section: Introductionmentioning

confidence: 99%

Effect of injection speed on the mechanical properties of isotactic polypropylene micro injection molded parts based on a nanoindentation test

Wang

Zhang

Jiang

et al. 2018

J of Applied Polymer Sci

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Isotactic polypropylene micro parts were molded at different injection speeds by microinjection molding. The morphology and micro structure were characterized by a polarizing microscope, and the mechanical properties of differently structured layers were characterized by nanoindentation experiments. The influence of injection speed on the nanoindentation mechanical properties of each structural layer of the micro parts was analyzed. The results showed that the mechanical properties of different layers were different, the modulus and hardness of the position near the core layer were largest, and the modulus and hardness of the position near the skin were smallest. It is compelling that the modulus and hardness of each layer decreased first and then increased as the injection speed increased under a higher melt temperature (240 °C). Meanwhile, the opposite trend was observed at a lower melt temperature (220 °C). This phenomenon can be attributed to the competitive mechanism of the shear heat effect and the disorientation effect. In addition, injection speed had a greater influence on the nanoindentation mechanical properties in the perpendicular direction than in the flow direction. This work systematically explored the relationship between the microstructure and the local mechanical properties, which can provide new insights for microinjection molding design in the future. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47329.

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“…Injection molding is regarded as the most important method for mass-producing plastic parts [1][2][3][4], and it is complicated because of steep thermal gradients and complex flow geometries; many studies have been conducted in this field [5][6][7][8][9][10]. e product weight of a plastic part is considered one of the most significant properties, especially for plastic parts with high precision requirements, such as plastic lenses.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Zhao

Dong

Zhang

et al. 2020

Advances in Polymer Technology

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Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.

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On-line measurement of clamping force for injection molding machine using ultrasonic technology

Cited by 46 publications

References 37 publications

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Effect of injection speed on the mechanical properties of isotactic polypropylene micro injection molded parts based on a nanoindentation test

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

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