Variable structure controller for plastic injection moulding system

This paper discusses the approach to design of combined ANN and PID temperature controller for a plastic injection moulding system. The proposed method is based on integration of a conventional PID (PI) controller and a multilayer ANN. At the initial stage of operation, the ANN is trained in offline mode to approximately identify the dynamic parameters of the regulator optimised in terms of speed of response and overshoot. Under routine operation mode the ANN control structure is responsible for the fast transients whereas PID (PI) controller provides the high accuracy at the steady state condition. The paper focuses on the structure switching mechanism and the influence on the transient accuracy. In order to verify the proposed approach, the control system having various types of heaters has been modelled and simulated in Matlab/Simulink. The data obtained from the experiment verified the developed model and confirmed the results of simulations

An ANN-based temperature controller for a plastic injection moulding system

This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board

Artificial neural network motor control for full-electric injection moulding machine

This paper proposes a new artificial neural network-based position controller for a full-electric injection moulding machine. Such a controller improves the dynamic characteristics of the positioning for hot runners, pin valve and the injection motors for varying moulding parameters. Practical experimental data and Matlab’s System Identification Toolbox have been used to identify the transfer functions of the motors. The structure of the artificial neural network, which used positioning error and speed of error, was obtained by numerical modelling in Matlab/Simulink. The artificial neural network was trained using back-propagation algorithms to provide control of the motor current thus ensuring the required position and velocity. The efficiency of the proposed ANN-based controller has been estimated and verified in Simulink using real velocity data and the position of the injection moulding machine and pin valve motors

Desenvolvimento de equipamento lúdico de processamento de plástico reciclado

This project aims to increase literacy related to plastics recycling, associated technological processes and the creation of new products made with this raw material. Plastic materials present and contribute to a shallow environmental impact throughout their life cycle, except at the end of life. If discarded into the environment, they will be a source of contamination for thousands of years. It is therefore essential, on one hand, to develop recycling processes and incorporate this raw material in new products and, on the other hand, to create acceptance of these products made with recycled raw material in society. The circular economy is an alternative to the current linear, make, use, dispose of, economy model, to keep resources in use for as long as possible, extract the most value from them while in use, and recover and regenerate products and materials at the end of their service life. The Precious Plastics project was started in 2013 by Dave Hakkens (Netherlands) and replicated in several locations worldwide. The University of Aveiro, in collaboration with Design Factory Aveiro and with the support of the project "INTEGRA@TEC -Transfer of integrated skills and generating business innovation in the Central Region". It developed a set of recycled plastics processing equipment in 2019, which integrated the Smart Plastic Lab, which has a pole in the Department of Mechanical Engineering. Students have already used the equipment, professors and researchers for several scientific-technological dissemination and exploration actions, namely activities of the Summer Academy of the University of Aveiro, hackathons of the OceanWisee project and several masters' works. They will now also be in Precious Plastics Aveiro, a project to be developed by the University of Aveiro. This project was funded by the Portuguese Institute for Sports and Youth through the Youth Participatory Budget Portugal, which will bring together new equipment adapted for better transportability to be taken to presentations. The project Precious Plastics Aveiro aims to create a creative recycling unit, with offsite activities developed by the Living Science Centre Factory and activities in the Departments of Mechanical Engineering and Environment and Planning, as well as in the Design Factory Aveiro.O presente projeto visa contribuir para o aumento da literacia relacionada com a reciclagem de plásticos, dos processos tecnológicos associados e da criação de novos produtos feitos com esta matéria-prima, de uma forma lúdica. Os materiais plásticos apresentam e contribuem para um muito baixo impacto ambiental ao longo do seu ciclo de vida, exceto no final de vida, pois se descartados para o meio ambiente serão fonte de contaminação por milhares de anos. Importa, pois, por um lado desenvolver processos de reciclagem e incorporação desta matéria-prima em novos produtos e por outro lado criar aceitação destes produtos feitos com matéria-prima reciclada na sociedade. É neste contexto que o presente projeto tem a sua génese, inspirado no projeto Precious Plastics, um projeto de desenvolvimento de equipamento de reciclagem de plástico aberto (“open source”), assente num conjunto de máquina e ferramentas que trituram, fundem e injetam plástico reciclado, permitindo a criação de novos produtos a partir de plástico reciclado em pequena escala. O projeto Precious Plastics iniciado em 2013 por Dave Hakkens (Países Baixos) tem vindo a ser replicado em vários pontos do mundo, tendo a Universidade de Aveiro, em parceria com a Design Factory Aveiro, apoiados pelo projeto “INTEGRA@TEC –Transferência de competências integradas e geradoras de inovação empresarial na Região Centro”, desenvolvido, em 2019, um conjunto de equipamentos de processamento de plásticos reciclados que integraram o Smart Plastic Lab, que dispõe de um polo no Departamento de Engenharia Mecânica da Universidade de Aveiro e um segundo polo na Design Factory Aveiro do Parque Ciência e Inovação. Os equipamentos serviram já diversas ações de disseminação e exploração cientifico-tecnológica, nomeadamente atividades da Academia de Verão da Universidade de Aveiro, hackathons do projeto OceanWise diversos trabalhos de mestrado. Serão agora enquadrados também no projeto Precious Plastics Aveiro, um projeto a ser desenvolvido pela Universidade de Aveiro. Este é financiado pelo Instituto Português do Desporto e Juventude por via do Orçamento Participativo Jovem Portugal, que juntará novos equipamentos, adaptados para uma maior transportabilidade, de modo a serem levados às escolas do ensino básico e secundário. O projeto Precious Plastics Aveiro tem como objetivo a criação de uma unidade de reciclagem criativa, com atividades deslocalizadas desenvolvidas pela Fábrica Centro de Ciência Viva, e atividades nos Departamentos de Engenharia Mecânica e de Ambiente e Ordenamento, assim como na Design Factory Aveiro.Mestrado em Engenharia Mecânic

An approach to integrating manufacturing data from legacy Injection Moulding Machines using OPC UA

To achieve the ambitions related with the concept of a Smart Factory, manufacturers of new industrial devices have been developing and releasing products capable of integrating themselves into fully-connected environments, with the communication capabilities and advanced specifications required. In these environments, the automatic retrieval of data across the shop floor is a must, allowing the analysis of machine performance for increased production quality and outputs. On most of the recently released industrial devices this machine data is readily available. However, the same is not true when using legacy devices. It is also well established that most SMEs are unable or do not intend to radically replace their industrial devices with this purpose only, since that would imply a high investment, and mainly because many of these legacy machines remain highly productive. That said, there is a need to develop integration methodologies for these legacy industrial devices and provide them with smart factory communication capabilities that make them suitable for the new Smart Factory environments. In this work, an approach is proposed, using as a case study an industrial shop floor, to integrate data from a range of injection moulding machines, from different generations and different models / manufacturers. This equipment diversity renders the automatic interconnection extremely challenging, but is also representative of many existing industrial scenarios. This research will contribute to the development of integration methodologies and, consequently, improve equipment compatibility. To apply these methodologies, information about specific machines within the shop floor was gathered, as well as their communication and I/O capabilities, together with other features deemed relevant. A trend in recently released machines can be identified, revealing a special focus on the use of OPC UA standard, making use of its address space based on the structured Euromap information models. On the other hand, the legacy devices mainly allow outputting a text file to an external storage unit connected to the machine, containing machine and injection cycles related information. Regarding the communication interfaces available, the Ethernet interface reveals to be the most common among the recently acquired machines, while USB is the main interface in older equipment. An experimental solution was developed for the presented case study, which uses the machine's USB interface to access these files at each injection cycle, mapping the acquired data to structured information model variables, according with Euromap specifications, and making it available through an OPC UA server address space. The developed server provides a standardized, interoperable, scalable, and secure approach for data exchange between the injection moulding machines and various OPC UA clients, allowing device monitoring and control during operation, as well as transmitting this data to higher-level management systems, e.g., MES and ERP systems. This solution shows that older legacy devices, available across the shop floors, can be retrofitted and integrated in Smart Factory scenarios, side-by-side with recently released equipment, giving production managers access to information needed to monitor and improve the production process, thus moving towards the Factories of the Future.info:eu-repo/semantics/publishedVersio

The compounding of short fibre reinforced thermoplastic composites

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.It is generally accepted that the mechanical properties of short fibre reinforced thermoplastics do not correspond with the high mechanical properties of fibres used to reinforce them. A study is made into the methods of compounding reinforcing fibres into thermoplastics to produce short fibre reinforced thermoplastics of enhanced properties. The initial method chosen for investigation is the twin screw extrusion compounding process. Variables such as fibre feeding arrangement and extrusion screw design are found to be factors influencing the properties of carbon and glass reinforced nylon 6,6. Use is made of computer programs to predict properties, assess compound quality and estimate fibre-matrix bond strength. Investigations indicate that the presence of reinforcing fibres with enhanced lengths does not result in the predicted property increases. The reasons for this shortfall are believed to lie in unfavourable fibre orientation in injection mouldings and the reduced strain to break of these materials. Short Kevlar reinforced thermoplastics are compounded and their mechanical properties assessed. The reasons for the poor mechanical properties for these materials are identified as a poor bond strength between fibre and matrix, the formation of points of weakness within the fibres by the compounding and moulding processes and the coiled arrangement of fibres present in injection mouldings. A method suitable for the routine assessment of fibre-matrix bond strength is used to examine combinations of fibre and thermoplastic matrix. A comparison is made of the values derived from this method with values calculated from stress-strain curves of injection mouldings. This allows an understanding of the nature of the fibre-matrix bond yielded by compounding and injection moulding steps. A description is given of a novel method designed to overcome the limitations of conventional compounding routes to produce long fibre reinforced injection moulding feedstock. Further work is necessary before this method is a feasible production technique

Thermal management in laminated die system

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control, Automation and Systems on August 2014, available online: http://dx.doi.org/10.1007/s12555-013-0348-6The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times

Thermal Management in Laminated Die Systems Using Neural Networks

The thermal control of a die is crucial for the development of high efficiency injection moulds. For successful thermal management, this research provides an effective control strategy to find sensor locations, identify thermal dynamic models, and design controllers. By applying a clustering method and sensitivity analysis, sensor locations are identified. The neural network and finite element analysis techniques enable the modeling to deal with various cycle-times for the moulding process and uncertain dynamics of a die. A combination of off-line training through finite element analysis and training using on-line learning algorithms and experimental data is used for the system identification. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-adaptive PID methods with backpropagation (BP) and radial basis function (RBF) neural networks to tune control parameters. Direct adaptive inverse control and additive feedforward control by adding direct adaptive inverse control to self-adaptive PID controllers are also provided. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times. Additionally, the improved cooling effectiveness of the conformal cooling channel designed in this study is presented by comparing with a conventional straight channel

Modular integration and on-chip sensing approaches for tunable fluid control polymer microdevices

228 p.Doktore tesi honetan mikroemariak kontrolatzeko elementuak diseinatu eta garatuko dira, mikrobalbula eta mikrosentsore bat zehazki. Ondoren, gailu horiek batera integratuko dira likido emari kontrolatzaile bat sortzeko asmotan. Helburu nagusia gailuen fabrikazio arkitektura modular bat frogatzea da, non Lab-on-a-Chip prototipoak garatzeko beharrezko fase guztiak harmonizatuz, Cyclic-Olefin-Polymer termoplastikozko mikrogailu merkeak pausu gutxi batzuetan garatuko diren, hauen kalitate industriala bermatuz. Ildo horretan, mikrogailuak prototipotik produkturako trantsizio azkar, erraz, errentagarri eta arriskurik gabeen bidez lortu daitezkeenetz frogatuko da

Evaluation of fatigue properties of thermoplastic elastomers (TPEs) for biomedical applications

El principal objetivo de este proyecto es evaluar cuál será el comportamiento a fatiga a largo plazo de diferentes materiales (TPEs), que se usarán en un futuro como implantes de corazón y estarán expuestos a distintas condiciones del medio: los fluidos del cuerpo como la sangre, la temperatura corporal, el pH, el movimiento del corazón, ... Para ello se precisa conocer y por tanto medir, los parámetros que afectarán a la vida de dichos materiales, como la fuerza o las tensiones a las que se expondrán, entre otros. Del mismo modo se requerirá realizar análisis estáticos y dinámicos (SLT y SILT), sabiendo manejar los softwares apropiados para la futura interpretación de los resultados