Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approac

Productivity analysis of horizontal directional drilling

The National Research Council of Canada reported that rehabilitation of municipal water systems between 1997 and 2012 would cost $28 billion (NRC, 2004). With the rapid increase of new installations, the need for replacement and repair of pipe utilities and also the demand for trenchless excavation methods, increase. This must be done with minimum disruption to public. One alternative to reduce disruption is to use horizontal directional drilling (HDD) for new pipe installation scenarios. Consequently, contractors, engineers, and decision makers are facing continuous challenges regarding to estimation of execution time and cost of new pipe installations, while using HDD. This is because productivity prediction and consequently the cost estimation of HDD involves a large number of objective and subjective factors that need to be considered. It is well known that prediction of both productivity and cost is an important process in establishing and employing management strategies for a construction operation. This calls for the need of developing a dedicated HDD productivity model that meets present day requirements of this area of construction industry. There are two main objectives of the current research. The first objective is to identify the factors that affect productivity of HDD operations. The second objective is to develop a productivity prediction model for different soil conditions. To achieve these two objectives a thorough literature review was carried out. Thereafter, data on potential factors on productivity were collected from HDD experts across North America and abroad. Following data collection, the current research identified managerial, mechanical, environmental and pipe physical conditions parameters operating in three types of soils: clay, rock and sandy soils. Prior to model development, Analytical Hierarchy Process (AHP) technique was used to classify and rank these factors according to their relative importance. A neurofuzzy (NF) approach is employed to develop HDD productivity prediction model for pipe installation. The merits of this approach are that it decreases uncertainties in results, addresses non-linear relationships and deals well with imprecise and linguistic data. The following eight factors were finally selected as inputs of the model to be developed: operator/ crew skills, soil type, drilling rig capabilities, machine conditions, unseen buried obstacles, pipe diameter, pipe length and site weather and safety conditions. The model is validated using actual project data. The developed NF model showed average validation percent of 94.7%, 82.3% and 86.7%, for clay, rock and sand, respectively. The model is also used to produce productivity curves (production rate vs. influencing factors) for each soil type. Finally, an automated user-friendly productivity prediction tool (HDD-PP) based on present NF model is developed to predict HDD productivity. This tool is coded in MatLab ® language using the graphical user interface tool (GUI). The tool was used to test a case study. It was proved to be helpful for contractors, consultants and HDD professionals in predicting execution time and to estimate cost of HDD projects during the preconstruction phase in the environment of imprecise and noisy inputs

Monitorización inteligente en tiempo real del acabado superficial de micro-piezas basado en el modelado híbrido incremental

Este trabajo propone la aplicación de una estrategia de modelado híbrido incremental (HIM) para la estimación en tiempo real de la rugosidad superficial en procesos de micromecanizado. Esta estrategia comprende fundamentalmente dos pasos. En primer lugar, se obtiene un modelo híbrido incremental representativo del proceso de micromecanizado. El resultado final de este modelo es una función de dos entradas (avance por diente cuadrático y vibración media cuadrática (rms) en el eje Z) y una salida (rugosidad superficial). En segundo lugar, se evalúa el modelo híbrido incremental en tiempo real para obtener la rugosidad superficial. El modelo se corrobora experimentalmente mediante su integración en un sistema embebido de monitorización en tiempo real del acabo superficial. La evaluación del prototipo demuestra una tasa de éxito en la estimación de la rugosidad superficial del 83%. Estos resultados son la base para el desarrollo de sistemas embebidos en la monitorización del acabo superficial de micro-piezas en tiempo real y el posterior desarrollo de una herramienta a nivel industria

Ajuste óptimo y automático de un sistema de control en cascada. Aplicación al seguimiento de trayectorias en servosistemas con fricción y zona muerta.

En la actualidad, el control en cascada continúa siendo una de las estrategias más utilizada en la industria de procesos y manufacturera. Los nuevos requisitos de precisión y robustez en los sistemas de control de posición y trayectoria para la fabricación de componentes en la micro escala, obligan a rediseñar los métodos de ajuste para hacer frente a no linealidades duras como la fricción y la zona muerta. Este trabajo presenta el diseño y aplicación de un método de ajuste automático basado en simulación y optimización para el ajuste de los parámetros de sistema de control cascada del lazo de control de posición y de velocidad de un servomecanismo en presencia de fricción, elasticidad y holgura. La optimización basada en el método de Nelder-Mead utiliza como función de coste u objetivo, la minimización del máximo error de posición durante la inversión en presencia de no linealidades. El estudio realizado en simulación, los experimentos en tiempo real en el control de trayectoria y los estudios de viabilidad demuestran la validez de la estrategia propuesta con una mejora de más de 10% en la reducción del máximo error de posición, y sienta las bases a la implementación a nivel industrial de método propuesto

Soft computing for tool life prediction a manufacturing application of neural - fuzzy systems

Tooling technology is recognised as an element of vital importance within the manufacturing industry. Critical tooling decisions related to tool selection, tool life management, optimal determination of cutting conditions and on-line machining process monitoring and control are based on the existence of reliable detailed process models. Among the decisive factors of process planning and control activities, tool wear and tool life considerations hold a dominant role. Yet, both off-line tool life prediction, as well as real tune tool wear identification and prediction are still issues open to research. The main reason lies with the large number of factors, influencing tool wear, some of them being of stochastic nature. The inherent variability of workpiece materials, cutting tools and machine characteristics, further increases the uncertainty about the machining optimisation problem. In machining practice, tool life prediction is based on the availability of data provided from tool manufacturers, machining data handbooks or from the shop floor. This thesis recognises the need for a data-driven, flexible and yet simple approach in predicting tool life. Model building from sample data depends on the availability of a sufficiently rich cutting data set. Flexibility requires a tool-life model with high adaptation capacity. Simplicity calls for a solution with low complexity and easily interpretable by the user. A neural-fuzzy systems approach is adopted, which meets these targets and predicts tool life for a wide range of turning operations. A literature review has been carried out, covering areas such as tool wear and tool life, neural networks, frizzy sets theory and neural-fuzzy systems integration. Various sources of tool life data have been examined. It is concluded that a combined use of simulated data from existing tool life models and real life data is the best policy to follow. The neurofuzzy tool life model developed is constructed by employing neural network-like learning algorithms. The trained model stores the learned knowledge in the form of frizzy IF-THEN rules on its structure, thus featuring desired transparency. Low model complexity is ensured by employing an algorithm which constructs a rule base of reduced size from the available data. In addition, the flexibility of the developed model is demonstrated by the ease, speed and efficiency of its adaptation on the basis of new tool life data. The development of the neurofuzzy tool life model is based on the Fuzzy Logic Toolbox (vl.0) of MATLAB (v4.2cl), a dedicated tool which facilitates design and evaluation of fuzzy logic systems. Extensive results are presented, which demonstrate the neurofuzzy model predictive performance. The model can be directly employed within a process planning system, facilitating the optimisation of turning operations. Recommendations aremade for further enhancements towards this direction

Arquitectura de Control Cognitivo Artificial usando una plataforma computacional de bajo coste.

Hoy en día, las principales líneas de investigación tanto en Europa como de EEUU a nivel industrial, abordan aspectos como la interacción hombre-robot y dotar de inteligencia a las máquinas, y por tanto tienen un papel fundamental a la hora de desarrollar cualquier propuesta. Una manera de dotar a las máquinas de conocimiento de la operación que realizan y su interacción con el resto del flujo productivo es la utilización de arquitecturas de control inteligente artificial. A pesar que dichas arquitecturas están dentro de las áreas de investigación priorizadas, aún existen muchas restricciones para su aplicación en la industria de manera general. En este trabajo se propone la emulación de las experiencias socio-cognitivas del ser humano para la toma de decisiones a escala industrial. Las técnicas basadas en Lógica Borrosa, la optimización heurística y las técnicas de auto-aprendizaje desempeñan cada día un papel más importante a la hora de crear los diferentes niveles o capas dentro del sistema. En este trabajo se implementa una arquitectura de control cognitiva artificial enfocada en cuatro aspectos fundamentales: capacidades de auto-aprendizaje y auto-optimización para la estimación; portabilidad y escalabilidad basada en plataformas computacionales de bajo coste; conectividad basada en middleware y enfoque basado en modelos para la estimación y predicción de estados. Finalmente se muestran algunos ensayos de validación en un proceso de microtaladrado que muestran una buena respuesta transitoria y un error de estado estacionario aceptable. Sin lugar a dudas, con la arquitectura de control cognitivo artificial propuesta se sientan las bases para su futura aplicación en una instalación industrial

The performance of ultrasonic pulse velocity on the prediction of tensile granite behaviour : a study based on artificial neural networks

The rehabilitation and repair of existing structures requires inspection. This generally includes in situ non-destructive testing. A very economical test is the non-destructive ultrasonic pulse velocity test (UPV). Lower information is available in the literature in relation to the use of this technique for the estimation of the tensile strength of materials. Therefore, this paper aims at using artificial neural networks (ANN) in the prediction of the mechanical behaviour of granites under tensile loading. The parameters under analysis are the tensile strength, displacement at peak stress and critical crack opening. For this, experimental results obtained from the physical and mechanical characterization under tension of distinct types of granites are combined and the performance of the developed models using the UPV index alone and combined with other physical parameters is analysed. The results of the ANNs models are also compared with respect to the results of regression models. The criteria used to evaluate the predictive performances of the models were the coefficient of correlation (R) and root mean square error (RMSE)

Supervisión inteligente de un proceso complejo basada en un modelo neuronal. Un caso real de aplicación.

An intelligent supervisory system inspired on a Neural Network Output Error model is presented herein. The application for predicting tool wear in a milling process is selected as a case study. The supervision block consists of a neural model, the weighted sum of squared residuals method and the tool condition index for a decision-making. This work shows the combined use of residual vector norm and the norm of the residual vector derivative to compute adaptive thresholds. The study analyses the influence of infinity and Euclidean norm on the results. Experimental tests are run in a professional machining centre under different cutting conditions using real-time data and new, half-worn and worn tools. The results show this supervisory system’s suitability

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

A Survey of Swarm Intelligence-based Optimization Algorithms for Tuning of Cascade Control Systems: Concepts, Models and Applications

Nowadays, cascade control is still one of the most used control strategies in the manufacturing and process industries. The new requirements of precision and robustness of position and trajectory tracking in control systems for manufacturing components at micro-scale, influenced by hard nonlinearities such as friction and backlash, have motivated the effort toward the development of algorithms for optimal tuning of control parameters. This paper presents a literature review of the algorithms and methods used to solve this problem. Swarm intelligence inspired optimization algorithms, namely particle swarm optimization algorithm (PSO) and grey wolf optimization algorithm (GWO), are applied for tuning of P-PI cascade controllers of CNC machine tool servo system in the presence of friction and backlash. The objective of the optimization is to minimize the maximum position error during the reversal of the axes. A comparative analysis of proposed algorithms with a standard industry-based fine tune (FT) method is also provided. Simulation study as well as real-world experiments carried out on a CNC machine tool controller show a remarkable improvement in the performance of the cascade control system using the proposed swarm intelligence-based strategy