An intelligent control system for die casting processes.

The objective of this thesis is to design an intelligent control system for die casting processes involving cooling of a die with multiple channels. The work consists of two parks. First, a correlation between die insert temperature and cooling water outlet temperature is established, which can be used to deduce local die surface temperatures without destructively inserting thermal sensors into a die from its back. Second, a new on-line thermal management scheme is proposed based on an intelligent real-time monitoring and control system (IRMCS) developed for a die insert containing multiple cooling channels. In this scheme, extra cooling water lines controlled by a pump and solenoid valves are hooked up to each established cooling channel. The system is capable of monitoring temperature signals from the die insert and flow rate signals from the cooling lines on the basis of its built-in control algorithms. Pump and solenoid valves can be actuated either automatically or manually to introduce additional cooling water to the die insert for preventing die overheating. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Y365. Source: Masters Abstracts International, Volume: 44-03, page: 1469. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Uncertainty quantification on industrial high pressure die casting process

High pressure die casting (HPDC) is a famous manufacturing technology in industry. This manufacturing process is simulated by commercial code to shed the light on the quality of casting product. The casting product quality might be affected by the uncertainty in the simulation parameter settings. Thus, the uncertainty quantification on HPDC process is significant to improve the casting quality and the manufacturing efficiency. In this work, three uncertainty quantifications and sensitivity analyses on the A380 aluminum alloy HPDC process of intermediate speed plate are performed. The material thermophysical properties, boundary conditions of the model, and operational as well as artificial parameters with their uncertainties, are considered as the inputs of interest. Uncertainty quantification and sensitivity analyses are investigated for the outputs of interest including percent volume of porosity result, percent volume of fraction solid less than 1, and the percent volume that solidified during multiple solidification times. The most influential input parameter for predicting the outputs of interest is the boundary condition of metal-die interfacial air gap

Predicción del desgaste de moldes de inyección de plástico y aluminio

219 p.En esta tesis doctoral se han abordado los principales mecanismos de desgaste que aparecen en los moldes de inyección de plástico y aluminio. Ambos consisten en inyectar a alta temperatura, presión y velocidad un fluido (plástico y aluminio respectivamente), en un molde cuya cavidad da forma a la pieza final tras el proceso de solidificación. Este molde tiene que aguantar cientos de miles de ciclos en este entorno agresivo, lo cual lleva a limitar la vida del molde debido al desgaste que sufren estos, requiriendo reparaciones y paradas de producción inesperadas. La formación del desgaste de los moldes se genera debido a distintos mecanismos de desgaste. Algunos de estos mecanismos son comunes para ambos casos de procesos de producción estudiados, como la erosión y la corrosión. Mientras, otros son específicos, como la abrasión en la inyección de plástico y la adhesión del aluminio (die soldering) y la fatiga térmica en la inyección de aluminio. A lo largo de esta Tesis doctoral se describe la metodología seguida para generar unos modelos de predicción de desgaste de estos mecanismos de desgaste de moldes a partir de la experimentación de laboratorio realizada

Robust Vision-based Thermal Control Systems with Industrial Applications

As multimodal camera networks have been deployed in various environment, image fusion is playing a critical role for better visual perception and process parameter measurement. The objective of the dissertation is to design robust vision-based thermal control systems to tolerate uncertainties for industrial automaton. To be specific, two new methods have been developed, one for robust shape fitting in visual images and another for packet loss recovery in thermal images. Firstly, an adaptive curve fitting technique is proposed based on prediction error sum of squares for the sampled data set containing outliers. The method converges very fast and superaccuracy can be obtained under certain conditions when compared with other methods. The method is applied to find an optimal curve of casting dies in the visual images. Secondly, the thermal image loss generated by network traffic from camera nodes to fusion center is modeled as a Markov chain. A graph cuts method is proposed to recover the loss based on thermal pattern classification. Simulation results show that thermal information can be partially retrieved, which may greatly increase the robustness of a thermal management system. The proposed methods are tested with a laboratory die casting process simulator with two visual cameras and one thermal camera. A simple fuzzy PID controller is designed to integrate the visual sensors into a control loop. The experimental results show that the homogeneity of the temperature distribution in the die may become achievable through the vision based thermal control system

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

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

A Review of Automotive Spare-Part Reconstruction Based on Additive Manufacturing

In the Industry 4.0 scenario, additive manufacturing (AM) technologies play a fundamental role in the automotive field, even in more traditional sectors such as the restoration of vintage cars. Car manufacturers and restorers benefit from a digital production workflow to reproduce spare parts that are no longer available on the market, starting with original components, even if they are damaged. This review focuses on this market niche that, due to its growing importance in terms of applications and related industries, can be a significant demonstrator of future trends in the automotive supply chain. Through selected case studies and industrial applications, this study analyses the implications of AM from multiple perspectives. Firstly, various types of AM processes are used, although some are predominant due to their cost-effectiveness and, therefore, their better accessibility and wide diffusion. In some applications, AM is used as an intermediate process to develop production equipment (so-called rapid tooling), with further implications in the digitalisation of conventional primary technologies and the entire production process. Secondly, the additive process allows for on-demand, one-off, or small-batch production. Finally, the ever-growing variety of spare parts introduces new problems and challenges, generating constant opportunities to improve the finish and performance of parts, as well as the types of processes and materials, sometimes directly involving AM solution providers