Control of rotational molding using adaptive fuzzy systems

Abu-Al-Nadi, Dia I.; Fara, Deeb Abu; Rawabdeh, Ibrahim A.; Crawford, R. J.

doi:10.1002/adv.20047

Cited by 13 publications

(11 citation statements)

References 10 publications

(6 reference statements)

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“…Nonetheless, these models enable the accurate prediction of the RM process and optimization of its parameters. Similarly advances in process control, especially advances in the sphere of automatic control for the RM process, may significantly improve the reproducibility of components over numerous runs [31][32][33]. Surface finish in RM is evaluated primarily in terms of the prevalence of porosity [38,57,60], severity of degradation [36] and warpage levels [37,79,80].…”

Section: Discussionmentioning

confidence: 99%

“…Automatic control of the RM cycle is made possible through the integration into this system, of an adaptive computer network. Data received from the transmitter is analyzed by a computer program which sends a signal to the machine to take the necessary regulatory action on the heating or cooling rates [31]. Other commercial mold temperature monitoring systems that work on a similar principle include: EZ Logger from Paladin, TempLogger, Roto -Paq and K-Control from 493 K. InfraRed Thermometry™ [32,33] is yet another system that is used to automatically regulate the RM process.…”

Section: Process Monitoring and Control Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Rotational molding: A review of the models and materials

Ogila¹,

Shao²,

Yang³

et al. 2017

Express Polym. Lett.

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This article surveys recent and not so recent literature in the field of rotational molding. The mechanisms of heat transfer, sintering and bubble removal are evaluated; as are degradation and dimensional stability. The parameters that affect the surface finish are highlighted and a number of the control systems available to the rotational molding process are mentioned. Improvements in molds and machinery, and the extent to which they reduce cycle times are also described. Finally, the range of materials available to the rotational molding process is examined and recent developments are highlighted. Of particular interest is the rotational molding of liquid polymer systems; which are shown to possess great potential for fulfilling many of rotational molding's surface quality requirements while simultaneously reducing cycle times.

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Section: Discussionmentioning

confidence: 99%

Section: Process Monitoring and Control Systemsmentioning

confidence: 99%

Rotational molding: A review of the models and materials

Ogila¹,

Shao²,

Yang³

et al. 2017

Express Polym. Lett.

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“…Crawford and Nugent (1992) have developed a control system that measures the internal air temperature and identifies when the powder starts sticking, melting and solidifying. Fuzzy logic has been used to relate the temperature to other manipulated parameters of the process (Abu-al-Nadi et al, 2005).…”

Section: General Process Descriptionmentioning

confidence: 99%

Rotational moulding of polymers

Torres¹

2009

Advances in Polymer Processing

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“…Another approach used to partially reject disturbances is trajectory tracking [6]. In this approach, a key measured process variable (such as the internal mold temperature), is tracked to a predefined set-point trajectory.…”

Section: Introductionmentioning

confidence: 99%

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

et al. 2019

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This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitude, impact energy and shear viscosity. The complete model is then deployed within a model-based control scheme that facilitates specifying on-spec products via limits on the quality variables. Further, this framework is demonstrated to be capable of rejecting raw material variability to achieve the desired specifications. To replicate raw material variability observed in practice, in this work, the raw material is obtained by blending the matrix resin with a resin of slightly different viscosity at varying weight fractions. Results obtained from experimental studies demonstrate the capability of the proposed model predictive control (MPC) in meeting process specifications and rejecting raw material variability.

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Control of rotational molding using adaptive fuzzy systems

Cited by 13 publications

References 10 publications

Rotational molding: A review of the models and materials

Rotational molding: A review of the models and materials

Rotational moulding of polymers

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

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