Adaptive control of the filling velocity of thermoplastics injection molding

Yang, Yi; Gao, Furong

doi:10.1016/s0967-0661(00)00060-5

Cited by 101 publications

(41 citation statements)

References 4 publications

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“…Regarding injection velocity, there are many adaptive control schemes reported in the publications, such as the self-tuning regulator (STR) and generalized predictive control (GPC), 34 sliding mode control (SMC), 35 fuzzy logic control (FLC), 36 and iterative learning control (ILC). 37,38 Technically speaking, all of the above control schemes have a common key component-the process model-whose accuracy significantly affects the final performance, even though the proper controller design can partially compensate for a model mismatch.…”

Section: Machine Controlmentioning

confidence: 99%

“…Furthermore, based on a linear AutoRegressive with eXogenous input (ARX) model, Yang and Gao 34 applied adaptive GPC to ram velocity control and compared it with a self-tuning poleplacement controller enhanced by several measures: antiwindup estimation to eliminate the estimation windup, cycle-to-cycle adaptation to improve the model convergence, and adaptive feedforward and profile shift to improve the tracking speed. They concluded that the adaptive GPC controller performed well over a wide range of process conditions.…”

Section: Machine Controlmentioning

confidence: 99%

“…Structure of an adaptive injection velocity control system. 34 Gao 36 used a fuzzy logic-based model to develop an FLC for injection velocity. Their experimental results revealed that the fuzzy logic controller worked well with different molds, materials, barrel temperatures, and injection velocity profiles, suggesting that the fuzzy logic controller has superior performance over the conventional PID controller in response speed, setpoint tracking, noise rejection, and robustness.…”

Section: Machine Controlmentioning

confidence: 99%

See 2 more Smart Citations

A review of current developments in process and quality control for injection molding

2005

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ABSTRACT:Injection molding is one of the most versatile and important manufacturing processes capable of mass-producing complicated plastic parts in net shape with excellent dimensional tolerance. Injection molding process and quality control has been an active research area for many years, as part quality and yield requirements become more stringent. This paper reviews the state-of-the-art research and development in injection molding control. It organizes prior studies into four categories, namely, process setup, machine control, process control, and quality control, and presents the distinction and connection of these different levels of control. This paper further reviews and compares the typical variables, models, and control methods that have been proposed and employed for those control tasks. Strictly speaking, real online quality control without human intervention has yet to be realized, primarily due to the lack of transducers for online, real time quality response measurement, and a robust model that correlates the control variables with quantitative quality measurements. Based on the research progress to date, this paper suggests that the different levels of control tasks have to be integrated into a multilevel quality control system, and

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Section: Machine Controlmentioning

confidence: 99%

Section: Machine Controlmentioning

confidence: 99%

Section: Machine Controlmentioning

confidence: 99%

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A review of current developments in process and quality control for injection molding

2005

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“…For monitoring diversified processes, a variety of knowledgeable strategies have been presented [4,[21][22][23] including subPLS modeling algorithm [24][25][26], recursive or adaptive PCA [27], model library based method [28], localized discriminant analysis [29], multiblock PLS, discriminant analysis [26], gaussian mixture model [30], and diversified statistical analysis method [20,31,32]. Among the existing nonlinear methods, kernel-based techniques have been successfully developed for tackling the nonlinear problem in recent years [33,34].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Diversified Processes Monitoring

Fan

et al. 2015

Mathematical Problems in Engineering

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The paper proposed a new method to classify and establish the monitoring model for diversified processes data with multiscale. The advantages of the proposed approach are listed as follows. (1) The issues of diversified processes data with multiscale are considered and the fault monitoring effect is enhanced. (2) From a new perspective, the common and specific characteristic subspaces are extracted to help simplify the structure of the monitoring model. (3) It makes the correlation between the common subspace itself and input-output dataset of each mode as close as possible. The effect of the proposed method has been shown in the Experiment Results section.

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“…1,2,5 In the Control Strategies section, the control background is described; in the Experimental section, the experimental conditions used in this paper are given; in the Results and Discussion section, the control results and some discussion are presented; and finally, the conclusions are drawn. …”

Section: Introductionmentioning

confidence: 99%

Injection molding control: From single cycle to batch control

2008

Self Cite

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Closed-loop control of key process variables in injection moldinghas become an essential part of the quality assurance system. Advanced control algorithms such as adaptive control and model predictive control have been adopted to deal with the inherent process nonlinearity and time-varying characteristics. These control algorithms are all focused on single-cycle control performance. Recently, a multicycle, two-dimensional model predictive learning control has been developed for batch process control. This method has been applied to injection molding. In this paper, the basic principles of these control algorithms are introduced, with the control performance demonstrated by injection velocity control.

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Adaptive control of the filling velocity of thermoplastics injection molding

Cited by 101 publications

References 4 publications

A review of current developments in process and quality control for injection molding

A review of current developments in process and quality control for injection molding

Nonlinear Diversified Processes Monitoring

Injection molding control: From single cycle to batch control

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