Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao, Peng; Zhang, Jianfeng; Dong, Zhengyang; Huang, Junye; Zhou, Hongwei; Fu, Jianzhong; Turng, Lih‐Sheng

doi:10.1155/2020/7023616

Cited by 64 publications

(41 citation statements)

References 187 publications

(254 reference statements)

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“…Compared with quality control, machine control is highly developed, especially in terms of commercial control on position, speed, temperature, and motion. The most widely used control algorithms include the PID control, linear quadratic optimal control, model predictive control, self‐tuning regulator, generalized predictive control, sliding mode control, fuzzy logic control, and iterative learning control 7 . The accuracy of these control algorithms is typically based on a mathematical model, regardless of whether it can describe the process well.…”

Section: Literature Reviewmentioning

confidence: 99%

Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality

Huang¹,

Ke²,

Liu

2020

J of Applied Polymer Sci

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Cavity pressure is one of the best indicators of injection molding conditions and thus has been used for quality prediction in the injection molding process. Also, the repeatability of the cavity pressure profile at each shot indicates the consistency of the part quality, which is easily affected by environmental changes, such as barrel temperature. To maintain quality consistency (such as part weight and geometrical dimensions) during mass production, this study proposed a novel method of the holding pressure adjustment to control the deviation in the cavity pressure distribution during each shot. Injection molding of a thin‐walled dumbbell‐shaped sample was performed to verify the proposed process, which proved the feasibility of this method for suppressing the influence of the barrel temperature changes on part quality.

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Section: Literature Reviewmentioning

confidence: 99%

Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality

Huang¹,

Ke²,

Liu

2020

J of Applied Polymer Sci

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“…Parameter optimization methods can be divided into two types, namely non-iterative optimization methods and iterative optimization methods. 5 Non-iterative optimization methods employ experimental data or a knowledge base to determine optimal parameters. Expert systems and the Taguchi method belong to the typical non-iterative optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

Dong

Zhao

Zheng³

et al. 2021

J of Applied Polymer Sci

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Intelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self-learning parameter optimization method named iterative gradient-approximation adaptive optimization (IGAO) method, which adopts the batch-to-batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation.The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.

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“…An important task in correct component production is optimization of the injection molding process in order to produce geometrically precise parts with specified mechanical properties. The authors pointed out that it is important to pay attention to methods and strategies that can be used for sensing, optimizing, and controlling the intelligent injection molding through feedback and machine learning, which is addressed by a number of world-renowned authors and research teams [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Physical Methods for the Detection of a Thermally Degraded Recycled Material in Plastic Parts Made of Polypropylene Copolymer

et al. 2021

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The paper deals with the possibility of applying physical methods to detect a thermally degraded recycled material in plastic parts made of polypropylene. Standard methods of evaluating the mechanical properties of the material under static tensile and bending stress, as well as under dynamic impact stress using the Charpy method, were used for the experimental measurements. The rheological properties of materials were monitored using a method involving measuring the melt flow index, while their thermal properties and oxidative stability were monitored using differential scanning calorimetry. Based on the methods used, it can be clearly stated that the most suitable technique for detecting thermally degraded recycled material in polypropylene is the method involving establishing the melt flow index. The bending test seems to be the most suitable method for detecting recycled material by measuring the material’s mechanical properties. Similarly to the melt volume flow rate (MVR) method, it was possible to unambiguously detect the presence of even a small amount of recycled material in the whole from measuring the material’s bending properties. It is clear from the results that in the short term, there may be no change in the useful properties of the parts, but in the long term the presence of degraded recycled material will have adverse consequences on their lifespan.

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Intelligent Injection Molding on Sensing, Optimization, and Control

Cited by 64 publications

References 187 publications

Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality

Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

Application of Physical Methods for the Detection of a Thermally Degraded Recycled Material in Plastic Parts Made of Polypropylene Copolymer

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