Development of a classification and coding system for computer-aided process planning

The chore of this work was to develop a Group Technology Classification Code which can represent the full gamut of simple, rotational parts. The automated coding plan is developed to alleviate the endeavor of the process planner to plan the tasks related to the manufacturing of a specific part. The 15 digits were devised from the Japanese KK-3 Classification and Coding system. The KK-3 System contains 21 digits. Our aim is to minimize the code length, and concurrently to eliminate the redundancies. As a result, a 15 digit G.T. Code is created. The proposed plan also generates the operation logic with the aid of the 15 digit G. I Code and the machines available in the database of the system. The program is designed to run on vax/vms 5.1. The program for the work has been written in Fortran - 77

Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite

Carbon fiber-reinforced plastic (CFRP) is gaining wide acceptance in areas including sports, aerospace and automobile industry . Because of its superior mechanical qualities and lower weight than metals, it needs effective and efficient machining methods. In this study, the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated for CFRP composite. For machining of CFRP, CNC turning operation with coated carbide tool is used. An ANFIS model with two MISO system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameter. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. However, Design of Experiments (DOE) confirmation of this experiment fails because of multi-collinearity problem in the dataset and insufficient experimental data points to predict the tool wear and surface roughness effectively using ANFIS methodology. Therefore, the result of this experiment do not provide a proper representation, and result in a failure to conform to a correct DOE approach

Intelligent Approaches for Energy-Efficient Resource Allocation in the Cognitive Radio Network

The cognitive radio (CR) is evolved as the promising technology to alleviate the spectrum scarcity issues by allowing the secondary users (SUs) to use the licensed band in an opportunistic manner. Various challenges need to be addressed before the successful deployment of CR technology. This thesis work presents intelligent resource allocation techniques for improving energy efficiency (EE) of low battery powered CR nodes where resources refer to certain important parameters that directly or indirectly affect EE. As far as the primary user (PU) is concerned, the SUs are allowed to transmit on the licensed band until their transmission power would not cause any interference to the primary network. Also, the SUs must use the licensed band efficiently during the PU’s absence. Therefore, the two key factors such as protection to the primary network and throughput above the threshold are important from the PU’s and SUs’ perspective, respectively. In deployment of CR, malicious users may be more active to prevent the CR users from accessing the spectrum or cause unnecessary interference to the both primary and secondary transmission. Considering these aspects, this thesis focuses on developing novel approaches for energy-efficient resource allocation under the constraints of interference to the PR, minimum achievable data rate and maximum transmission power by optimizing the resource parameters such as sensing time and the secondary transmission power with suitably selecting SUs. Two different domains considered in this thesis are the soft decision fusion (SDF)-based cooperative spectrum sensing CR network (CRN) models without and with the primary user emulation attack (PUEA). An efficient iterative algorithm called iterative Dinkelbach method (IDM) is proposed to maximize EE with suitable SUs in the absence of the attacker. In the proposed approaches, different constraints are evaluated considering the negative impact of the PUE attacker on the secondary transmission while maximizing EE with the PUE attacker. The optimization problem associated with the non-convex constraints is solved by our proposed iterative resource allocation algorithms (novel iterative resource allocation (NIRA) and novel adaptive resource allocation (NARA)) with suitable selection of SUs for jointly optimizing the sensing time and power allocation. In the CR enhanced vehicular ad hoc network (CR-VANET), the time varying channel responses with the vehicular movement are considered without and with the attacker. In the absence of the PUE attacker, an interference-aware power allocation scheme based on normalized least mean square (NLMS) algorithm is proposed to maximize EE considering the dynamic constraints. In the presence of the attacker, the optimization problem associated with the non-convex and time-varying constraints is solved by an efficient approach based on genetic algorithm (GA). Further, an investigation is attempted to apply the CR technology in industrial, scientific and medical (ISM) band through spectrum occupancy prediction, sub-band selection and optimal power allocation to the CR users using the real time indoor measurement data. Efficacies of the proposed approaches are verified through extensive simulation studies in the MATLAB environment and by comparing with the existing literature. Further, the impacts of different network parameters on the system performance are analyzed in detail. The proposed approaches will be highly helpful in designing energy-efficient CRN model with low complexity for future CR deployment

Optimazation of marine sediments characterization via statistical analysis

The task of geotechnical site characterization includes defining the layout of ground units and establishing their relevant engineering properties. This is an activity in which uncertainties of different nature (inherent, experimental, of interpretation…) are always present and in which the amount and characteristics of available data are highly variable. Probabilistic methodologies are applied to assess and manage uncertainties. A Bayesian perspective of probability, that roots probability on belief, is well suited for geotechnical characterization problems, as it has flexibility to handle different kind of uncertainties and highly variable datasets –in quality and quantity. This thesis addresses different topics of geotechnical site characterization from a probabilistic perspective, with emphasis on offshore investigation, on the Cone Penetration Test (CPTu) and on Bayesian methodologies.The first topic addresses soil layer delineation based on CPT(u) data. The starting point is the recognition that layer delineation is problem-oriented and has a strong subjective component. We propose a novel CPTu record analysis methodology which aims to: a) elicit the heuristics that intervene in layer delineation, facilitating communication and coherence in interpretation b) facilitate probabilistic characterization of the identified layers c) is simple and intuitive to use. The method is based on sequential distribution fitting in conventionally accepted classification spaces (Soil Behavior Type charts). The proposed technique is applied at different sites, illustrating how it can be related to borehole observations, how it compares with alternative methodologies and how it can be extended to create cross-site profiles. The second topic addresses strain-rate corrections of dynamic CPTu data. Dynamic CPTu impact on the seafloor and are very agile characterization tools. However, they require transformation to equivalent quasi-static results that can be conventionally interpreted. Up to now the necessary corrections are either too vague or require the acquisition of paired dynamic and quasi-static CPTu records (i.e., same location’s acquisition). A Bayesian methodology is applied to derive strain-rate coefficients in a more general setting, one in which some quasi-static CPTu records are available in the study area, but they need not be paired to any converted dynamic CPTu. Application to a case study offshore Nice shows that the results match those obtained using paired tests. Furthermore, strain rate correction coefficients and transformed quasi-static profiles are expressed in probabilistic terms.The third topic addressed is the optimization of soil unit weight prediction from CPTu readings. A Bayesian Mixture Analysis is applied to a global database to identify hidden soil classes within it. The goal is to improve the accuracy of regressions between geotechnical parameters obtained by exploiting the database. The method is applied to predict soil unit weight from CPTu data, a problem that has intrinsic practical interest but it is also representative of difficulties faced by a larger class of problems in geotechnical regression. Results highlight a decrease of systematic transformation uncertainty and an improve of accuracy of soil unit weight prediction from CPTu at new sites. In a final application we present a probabilistic earthquake-induced landslide susceptibility map of the South-West Iberian margin. A simplified geotechnical pixel-based slope stability model is considered to whole study area within which the key stability model parameters are treated as random variables. Site characterization at the regional scale combines a global database with available geotechnical data through a Bayesian scheme. Outputs (landslide susceptibility maps) are derived from a reliability-based design procedure (Montecarlo simulations) providing a robust landslide susceptibility prediction at the site according to Receiver Operating Curve (ROC).La caracterización geotécnica de un emplazamiento incluye la definición de la disposición de las unidades de suelo y el establecimiento de sus propiedades de ingeniería relevantes. Es una actividad en la que siempre están presentes incertidumbres y en la que la cantidad y las caracteristicas de los datos disponibles son muy variables. Para evaluar y gestionar las incertidumbres se aplican metodologías probabilísticas. Una perspectiva bayesiana de la probabilidad es muy adecuada para la caracterización geotécnica, ya que tiene flexibilidad para manejar incertidumbres y datos muy variables. Esta tesis aborda diferentes temas de caracterización geotécnica desde una perspectiva probabilística, con énfasis en la investigación en alta mar, en el ensayo de penetración de cono (CPTu) y en las metodologías bayesianas El primer tema aborda la delineación de la capa de suelo basada en los datos CPT(u). El punto de partida es el reconocimiento de que la delineación de capas tiene un fuerte componente subjetivo. Proponemos una novedosa metodología de análisis de registros CPTu que tiene como objetivo: a) expresar la heurística que interviene en la delineación de capas, facilitando la comunicación en la su interpretación b) facilitar la caracterización probabilística de las capas identificadas c) uso sencillo e intuitivo. El método se basa en el ajuste de distribuciones secuenciales en espacios de clasificación (tablas de comportamiento del suelo). La técnica propuesta se aplica en diferentes emplazamientos, ilustrando cómo puede relacionarse con sondeos, cómo se compara con metodologías alternativas y cómo puede ampliarse para crear perfiles entre emplazamientos. El segundo tema aborda las correcciones de la velocidad de deformación de los datos del CPTu dinámico (que impactan en el fondo marino y son herramientas de caracterización muy ágiles). Sin embargo, requieren una transformación a resultados equivalentes que puedan ser interpretados convencionalmente. Hasta ahora las correcciones necesarias son vagas o requieren la adquisición de CPTu dinámicos y cuasi-estáticos emparejados. Se aplica una metodologia bayesiana para derivar los coeficientes de velocidad de deformación en un entorno más general, en el que se dispone de algunos registros de CPTu cuasi­estáticos en la zona de estudio, pero no es necesario emparejarlos con ningún CPTu dinámico convertido. La aplicación a un estudio de caso en el mar de Niza muestra que los resultados coinciden con los obtenidos mediante pruebas emparejadas. El tercer tema abordado es la optimización de la predicción del peso unitario del suelo a partir de las lecturas del CPTu. Se aplica un análisis de mezclas bayesiano a una base de datos global para identificar las clases de suelo ocultas en ella. El objetivo es mejorar la precisión de las regresiones entre los parámetros geotécnicos obtenidos explotando la base de datos. El método se aplica a la predicción del peso unitario del suelo a partir de los datos del CPTu. Los resultados destacan una disminución de la incertidumbre sistemática de la transformación y una mejora de la precisión de la predicción del peso unitario del suelo a partir de CPTu en nuevos sitios. En una aplicación final presentamos un mapa probabilistico de susceptibilidad a los deslizamientos de tierra inducidos por terremotos en el margen suroeste de la Península Ibérica. Se considera un modelo geotécnico simplificado de estabilidad de laderas basado en píxeles para toda el área de estudio, dentro del cual los parámetros clave del modelo de estabilidad se tratan como variables aleatorias. La caracterización a escala regional combina una base de datos global con los datos geotécnicos disponibles mediante un esquema bayesiano. Mapas de susceptibilidad a los corrimientos de tierra se derivan de un procedimiento de diseño basado en la fiabilidad que proporciona una predicción robusta de la susceptibilidad a deslizamientos de tierra en el sitio de acuerdo con la curva operativa del receptor (ROC).Postprint (published version