Chamil Abeykoon

A Novel Soft Sensor for Real-Time Monitoring of the Die Melt Temperature Profile in Polymer Extrusion

Polymer extrusion is the most fundamental technique for processing polymeric materials, and its importance is increasing due to the rapid growth of worldwide demand for polymeric materials. However, the process thermal monitoring is experiencing several problems resulting in poor process diagnostics and control. Most of the existing process thermal monitoring methods in industry only provide point/bulk measurements, which are less detailed and low in accuracy. Physical thermal profile measurements across the melt flow may not be industrially compatible due to their complexity, access requirements, invasiveness, etc. Therefore, inferential thermal profile monitoring techniques are invaluable for obtaining detailed, accurate, and industrially compatible measurements and, hence, to achieve improved process control. In this paper, a novel soft sensor strategy is proposed to predict the real-time temperature profile across the die melt flow in polymer extrusion for the first time in industry or research. It is capable of determining the melt temperature at a number of die radial positions only based on six readily measurable process parameters. A comparison between the simulation results of the novel melt temperature profile prediction soft sensor and the experimental measurements showed that the soft sensor can predict the real-time melt temperature profile of the die melt flow with good accuracy. Therefore, this will offer a promising solution for making real-time melt temperature profile measurements noninvasively in polymer extrusion, and also, it should be applicable to other polymer processes only with a few modifications. Moreover, this technique should facilitate in developing an advanced process thermal control strategy.

Modelling the effects of operating conditions on motor power consumption in single screw extrusion

Extrusion is one of the most important production methods in the plastics industry and is involved in the production of a large number of plastics commodities. Being an energy intensive production method, process energy efficiency is of major concern and selection of the most energy efficient processing conditions is a key aim to reduce operating costs. Extruders consume energy through motor operation (i.e. drive to screw), the barrel heaters and also for cooling fans, cooling water pumps, gear pumps, screen pack changing devices etc. Typically the drive motor consumes more than one third of the total machine energy consumption. This study investigates the motor power consumption based on motor electrical variables (only for direct current (DC) motors) and new models are developed to predict the motor power consumption from easily measurable process settings for a particular machine geometry. Developed models are in good agreement with training and unseen data by representing the actual conditions with more than 95% accuracy. These models will help to determine the effects of individual process settings on the drive motor energy consumption and optimal motor energy efficient settings for single screw extruders.

A Novel Model-Based Controller for Polymer Extrusion

Extrusion is a fundamental technique of processing polymeric materials, and the thermal homogeneity of the process melt output is a major concern for high-quality extruded products. Therefore, accurate process thermal monitoring and control are highly invaluable for product quality control. However, most of the industrial extruders use conventional thermocouples whose measurements are limited to a single point and are highly influenced by barrel metal wall temperature. It has shown that the melt temperature varies considerably with the die radial position, and hence, point-based measurements are not sufficient to determine the actual thermal stability across the melt flow. Therefore, thermal control techniques based on such point/bulk measurements may be limited in performance. In addition, the majority of process thermal control methods are based on linear models and are not capable of dealing with process nonlinearities. In this study, a review of the previous work relating to extruder melt temperature control is presented while identifying their limitations. A novel model-based control approach is then proposed to control the polymer extrusion process incorporating a melt temperature profile prediction soft sensor and fuzzy logic. The results show that the proposed controller is good in achieving the desired average melt temperature across the melt flow while minimizing the melt temperature variance. The adjustments made by the controller to the manipulated variables confirmed that it has the capability of adjusting the suitable variables, depending on the different situations encountered. Therefore, this will be a promising alternative to linear control techniques and control techniques based on point/bulk thermal measurements which are common in the present industry.

Extruder Melt Temperature Control with Fuzzy Logic

Abstract In polymer extrusion, the delivery of a melt which is homogenous in composition and temperature is paramount for achieving high quality extruded products. However, advancements in process control are required to reduce temperature variations across the melt flow which can result in poor product quality. The majority of thermal monitoring methods provide only low accuracy point/bulk melt temperature measurements and cause poor controller performance. Furthermore, the most common conventional proportional-integral-derivative controllers seem to be incapable of performing well over the nonlinear operating region. This paper presents a model-based fuzzy control approach to reduce the die melt temperature variations across the melt flow while achieving desired average die melt temperature. Simulation results confirm the efficacy of the proposed controller.

Modelling of melt pressure development in polymer extrusion: Effects of process settings and screw geometry

Melt pressure is one of the most important process parameters in polymer extrusion and it is also closely related to the product quality. However, it is not directly controllable and is affected in a complex manner by changing other process operating conditions such as screw rotation speed and set temperatures. The ability to predict such parameter would be a powerful tool to aid process design and optimisation. However, only a few practical process models are available to predict the melt pressure based on process settings in polymer extrusion. This paper describes new nonlinear static and linear dynamic models which have been developed to explore the effects of process settings and screw geometry on melt pressure development in single screw extrusion. A computationally efficient linear-in-the-parameters modelling technique was used in model development and the resultant models show satisfactory performance in predicting the melt pressure with good accuracy over a wide operating window.

Monitoring and Modelling of the Effects of Process Settings and Screw Geometry on Melt Pressure Generation in Polymer Extrusion

Melt pressure is one of the most important process parameters in polymer extrusion and is closely related to product quality. However, it is not directly controllable and may be affected in a complex manner by changing other process operating conditions such as screw speed and barrel set temperatures. The ability to predict such parameters would be a powerful tool to aid process design and optimisation. However, only a few practical process models are currently available to predict melt pressure based on process settings in polymer extrusion. This paper describes new non–linear static and linear dynamic models that have been developed to explore the effects of process settings and screw geometry on melt pressure development in single screw extrusion. The models developed predict the melt pressure with good accuracy over a wide operating window. Investigations made using these models together with a frequency analysis of the measured signals showed that the melt pressure is influenced by both process settings and screw geometry.

Dynamic modelling of die melt temperature profile in polymer extrusion

The extrusion process is one of the main methods of processing polymeric materials and thermal homogeneity of the process output presents a major challenge for high quality extruded products. Therefore, accurate process thermal monitoring and control are highly desirable. However, most of the industrial extruders use conventional single point thermocouples for thermal monitoring although their measurements are highly affected by barrel metal wall temperature. Moreover, it has been shown that the melt temperature changes considerably with the die radial position and point based measurements are not sufficient to determine the actual process thermal stability and hence to control the thermal homogeneity of melt output. Conversely, the majority of process thermal control methods are based on linear models and are not capable of dealing with process nonlinearities. In this work, a die melt temperature profile was monitored by a thermocouple mesh technique and the data obtained was used to formulate a new nonlinear dynamic model to predict the die melt temperature profile in a single screw extruder. The model is in good agreement with the measured data and offers a promising thermal monitoring technique which can be used in real-time for a thermal profile based control framework in polymer extrusion.

Investigation of the Effect of Processing Parameters on 3D Printed Structures

Sandwich panel with lattice core for aircraft anti-ice system made by Selective Laser Melting / Varetti, Sara; Ferro, CARLO GIOVANNI; Casini, ANDREA EMANUELE MARIA; Mazza, Andrea; Maggiore, Paolo; Lombardi, Mariangela. In: INTERNATIONAL JOURNAL OF ADVANCEMENTS IN TECHNOLOGY. ISSN 0976-4860. ELETTRONICO. 9(2018), pp. 73-73. ((Intervento presentato al convegno 2nd International Conference on 3D Printing Technology and Innovations tenutosi a London (UK) nel March 19-20, 2018. Original Sandwich panel with lattice core for aircraft anti-ice system made by Selective Laser MeltingThe advent of additive manufacturing techniques, namely Fused Deposition Modeling (FDM), holds many promising prospects for medical applications, from tailored polypills for personalized medicine to patient-specific implants. However, the lack of pharmaceutically-acceptable materials that possess suitable properties for FDM is the main issue standing in the way of turning FDM into a commercially viable process. And although a number of research efforts has demonstrated the feasibility of using blends of pharmaceutically relevant polymers to print pharmaceutical dosage forms, there remains littleto-no investigation into the critical parameters that govern the feasibility of an FDM process. Mechanical properties of the filament used in FDM is one such critical parameter; part of the filament feeding process involves rotating gears pushing the filament into a pinhole slit that leads on to the heating element of the printer. Trial and error attempts at feeding various inhouse prepared filaments to the printer revealed that filaments need to possess specific mechanical properties; filaments which are too brittle will fracture inside the print head causing a blockage, filaments which are too deformable will coil around the conveyer gears without threading into the melting zone. This presentation outlines an in-house developed method to identify the desired mechanical properties for FDM filament: A TA.XT 2 Texture Analyzer fitted with an in-house prepared rig loosely based on the spaghetti flexure rig was used to quantify forces required to deform a number of commercial and in-house filaments. Principal Component Analysis (PCA) was used to sort the data collected from the texture analysis and categorize the various filaments into feedable and non-feedable. The method was then employed to evaluate the feedability of an ibuprofen formulation to verify its suitability as a method to test the mechanical properties of filaments.W 3D printing technology research accelerating year by year and with increasing number of applications of adaptive manufacturing in final products, engineers face the problem of having access to realiable design codes for elements manufactured with those new technologies. By the very nature of the 3D printing technology, manufactured elements are characterized by high degree of anisotropy of strength properties. The aim of the presented research is to establish reliable testing protocol for assesment of anisotropy of mechanical behaviour and to create data bases of experimental results for validation and calibration of numerical models. Besides traditional experimental techniques, new techniques based on Digtal Image Correlation (DIC) and in house developed software are incorporated into the experimental pipeline. In the presented research relatively, simple test cases of specimens under uniaxial tension and compression were analyzed. That allowed for simpler correlation of results from strength testing machine, DIC analysis and simple mechanical model, as well as clearly showed anisotropy effects. The rough surface of printed specimens turned out to be ideal for DIC measurements. The results obtained can be used to calibrate numerical models before testing more complex cases.This conference abstract is published by OMICS International under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Soft sensing of melt temperature in polymer extrusion

Precise monitoring techniques are invaluable to any process for diagnosing its operational health, safety concerns and also for achieving good process control. In polymer extrusion, it is quite difficult to visually observe the melt inside barrel during the process operation and hence the level of control of the process operational quality is highly dependent upon the process monitoring techniques. Currently, a number of physical sensing devices are widely available in industry for monitoring of parameters such as melt temperature, melt pressure, screw speed and so forth. However, there are some limitations to use physical sensors in process measurements due to several constraints such as their access requirements, disruptive effects on the melt flow, fragility, complexity, etc. Thus, the application of soft sensing techniques should be highly useful for improved process monitoring and hence for advanced process control. In this work, a general discussion is made on the soft sensors and soft sensing applications in polymer extrusion. Then, a soft sensor concept is proposed for the die melt temperature profile prediction in polymer extrusion. The simulation results showed that the proposed technique can predict the temperature profile across the melt flow in real-time with good accuracy. Eventually, the importance of developing of such soft sensing techniques is discussed while providing some of the possible directions for future research.

Monitoring and modelling of the energy consumption in polymer extrusion

Extrusion is one of the fundamental methods in polymer processing and is involved in the production of various commodities in diverse industrial sectors. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reduce operating expenses. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the dominant energy consuming device in a polymer processing extruder while barrel/die heaters are responsible for the second largest energy demand. This study was focused on investigating the total energy demand of an extrusion plant while identifying ways to optimise the energy consumption. Total energy consumption of a single screw extruder was measured over different processing conditions and then modelled as a function of a number of major process variables. The results show that the extruder energy demand is heavily coupled with machine, material and process parameters. Also, the proposed models show excellent agreement with the experimental measurements and hence these should be useful in optimising process energy efficiency.