Low-cost Process monitoring for polymer extrusion

Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.

Characterisation of Bioresorbable Composite Performance for Validation of New On-Line Process Monitoring Technology

There is an increasing interest in the biomedical field to create implantable medical devices to provide a temporary mechanical function for use inside the human body. In many of these applications bioresorbable polymer composites using PLLA with β-TCP , are increasingly being used due to their biocompatability, biodegradability and mechanical strength.1,3 These medical devices can be manufactured using conventional plastics processing methods such as injection moulding and extrusion, however there is great need to understand and control the process due to a lack of knowledge on the influence of processing on material properties. With the addition of biocompatible additives there is also a requirement to be able to predict the quality and level of dispersion within the polymer matrix. On-line UV-Vis spectroscopy has been shown to monitor the quality of fillers in polymers. This can eliminate time consuming and costly post-process evaluation of additive dispersion. The aim of this work was to identify process and performance relationships of PLLA/β-TCP composites with respect to melt-extrusion conditions. This is part of a wider study into on-line process monitoring of bioresorbable polymers as used in the medical industry.
These results show that final properties of the PLLA/ β-TCP composite are highly influenced by the particle size and loading. UV-Vis spectroscopy can be used on-line to monitor the final product and this can be utilised as a valuable tool for quality control in an application where consistent performance is of paramount importance.

Development of a multivariable online monitoring system for the thermoforming process

The thermoforming industry has been relatively slow to embrace modern measurement technologies. As a result researchers have struggled to develop accurate thermoforming simulations as some of the key aspects of the process remain poorly understood. For the first time, this work reports the development of a prototype multivariable instrumentation system for use in thermoforming. The system contains sensors for plug force, plug displacement, air pressure and temperature, plug temperature, and sheet temperature. Initially, it was developed to fit the tooling on a laboratory thermoforming machine, but later its performance was validated by installing it on a similar industrial tool. Throughout its development, providing access for the various sensors and their cabling was the most challenging task. In testing, all of the sensors performed well and the data collected has given a powerful insight into the operation of the process. In particular, it has shown that both the air and plug temperatures stabilize at more than 80C during the continuous thermoforming of amorphous polyethylene terephthalate (aPET) sheet at 110C. The work also highlighted significant differences in the timing and magnitude of the cavity pressures reached in the two thermoforming machines. The prototype system has considerable potential for further development. 

Second-Order Training of Adaptive Critics for Online Process Control

A new algorithm for training of nonlinear optimal neuro-controllers (in the form of the model-free, action-dependent, adaptive critic paradigm). Overcomes problems with existing stochastic backpropagation training: need for data storage, parameter shadowing and poor convergence, offering significant benefits for online applications.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

Dynamic in situ optical and magneto-optical monitoring of the growth of Co-Pd multilayers

In situ ellipsometry and Kerr polarimetry have been used to follow the continuous evolution of the optical and magneto- optical properties of multiple layers of Co and Pd during their growth. Films were sputter deposited onto a Pd buffer layer on glass substrates up to a maximum of N = 10 bi-layer periods according to the scheme glass/Pd(10)Ar x (0.3Co/3Pd) (nm). Magnetic hysteresis measurements taken during the deposition consistently showed strong perpendicular anisotropy at all stages of film growth following the deposition of a single monolayer of Co. Magneto-optic signals associated with the normal-incidence polar Kerr effect indicated strong polarization of Pd atoms at both Co-Pd and Pd-Co interfaces and that the magnitude of the complex magneto-optic Voigt parameter and the magnetic moment of the Pd decrease exponentially with distance from the interface with a decay constant of 1.1 nm(- 1). Theoretical simulations have provided an understanding of the observations and allow the determination of the ultrathin- film values of the elements of the skew-symmetric permittivity tensor that describe the optical and magneto-optical properties for both CO and Pd. Detailed structure in the observed Kerr ellipticity shows distinct Pd-thickness-dependent oscillations with a spatial period of about 1.6 nm that are believed to be associated with quantum well levels in the growing Pd layer.

Monitoring treatment fidelity in a randomised controlled trial of a complex intervention.

Aim. This paper is a report of a study to describe how treatment fidelity is being enhanced and monitored, using a model from the National Institutes of Health Behavior Change Consortium. Background. The objective of treatment fidelity is to minimize errors in interpreting research trial outcomes, and to ascribe those outcomes directly to the intervention at hand. Treatment fidelity procedures are included in trials of complex interventions to account for inferences made from study outcomes. Monitoring treatment fidelity can help improve study design, maximize reliability of results, increase statistical power, determine whether theory-based interventions are responsible for observed changes, and inform the research dissemination process. Methods. Treatment fidelity recommendations from the Behavior Change Consortium were applied to the SPHERE study (Secondary Prevention of Heart DiseasE in GeneRal PracticE), a randomized controlled trial of a complex intervention. Procedures to enhance and monitor intervention implementation included standardizing training sessions, observing intervention consultations, structuring patient recall systems, and using written practice and patient care plans. The research nurse plays an important role in monitoring intervention implementation. Findings. Several methods of applying treatment fidelity procedures to monitoring interventions are possible. The procedure used may be determined by availability of appropriate personnel, fiscal constraints, or time limits. Complex interventions are not straightforward and necessitate a monitoring process at trial stage. Conclusion. The Behavior Change Consortium’s model of treatment fidelity is useful for structuring a system to monitor the implementation of a complex intervention, and helps to increase the reliability and validity of evaluation findings.