A review of geometry representation and processing methods for cartesian and multiaxial robot-based additive manufacturing

Nowadays, robot-based additive manufacturing (RBAM) is emerging as a potential solution to increase manufacturing flexibility. Such technology allows to change the orientation of the material deposition unit during printing, making it possible to fabricate complex parts with optimized material distribution. In this context, the representation of parts geometries and their subsequent processing become aspects of primary importance. In particular, part orientation, multiaxial deposition, slicing, and infill strategies must be properly evaluated so as to obtain satisfactory outputs and avoid printing failures. Some advanced features can be found in commercial slicing software (e.g., adaptive slicing, advanced path strategies, and non-planar slicing), although the procedure may result excessively constrained due to the limited number of available options. Several approaches and algorithms have been proposed for each phase and their combination must be determined accurately to achieve the best results. This paper reviews the state-of-the-art works addressing the primary methods for the representation of geometries and the subsequent geometry processing for RBAM. For each category, tools and software found in the literature and commercially available are discussed. Comparison tables are then reported to assist in the selection of the most appropriate approaches. The presented review can be helpful for designers, researchers and practitioners to identify possible future directions and open issues

CAD/CAM integration based on machining features for prismatic parts

The development of CAD and CAM technology has significantly increased efficiency in each individual area. The independent development, however, greatly restrained the improvement of overall efficiency from design to manufacturing. The simple integration between CAD and CAM systems has been achieved. Current integrated CAD/CAM systems can share the same geometry model of a product in a neutral or proprietary format. However, the process plan information of the product from CAPP systems cannot serve as a starting point for CAM systems to generate tool paths and NC programs. The user still needs to manually create the machining operations and define geometry, cutting tool, and various parameters for each operation. Features play an important role in the recent research on CAD/CAM integration. This thesis investigated the integration of CAD/CAM systems based on machining features. The focus of the research is to connect CAPP systems and CAM systems by machining features, to reduce the unnecessary user interface and to automate the process of tool path preparation. Machining features are utilized to define machining geometries and eliminate the necessity of user interventions in UG. A prototype is developed to demonstrate the CAD/CAM integration based on machining features for prismatic parts. The prototype integration layer is implemented in conjunction with an existing CAPP system, FBMach, and a commercial CAD/CAM system, Unigraphics. Not only geometry information of the product but also the process plan information and machining feature information are directly available to the CAM system and tool paths can be automatically generated from solid models and process plans

Process planning for an Additive/Subtractive Rapid Pattern Manufacturing system

This dissertation presents a rapid manufacturing process for sand casting patterns using a hybrid additive/subtractive approach. This includes three major areas of research that will enable highly automated process planning; a critical need for a rapid methodology. The first research area yields a model for automatically determining the locations of layers, given the slab height, material types and part geometry. Layers are chosen such that it will avoid catastrophic failures and poor machining conditions in general. First, features that are possible thin material machining positions are defined, and methods for detecting these feature positions from an STL model are studied. Next, a layer thickness calculation model is presented according to positions of these features. The second area focuses on tools and parameters for the subtractive side of processing each layer. A tool size and machining parameter selection model is presented that can automatically select tool sizes and machining parameters, given layer thickness, part geometry, and material types. Machining strategies and related machining parameters are studied first. Then the method for Stepdown parameter calculation is presented. Finally, an algorithm based on both accessibility and machining efficiency is proposed for the selection of tool sizes for the rough cutting operation, finish cutting operation and optional semi-rough cutting operation. The final research area focuses on a cutting force analysis for thin material machining with additional layer thickness & tool size interaction. Popular cutting force models are reviewed, and a suitable model for cutting force calculation in this process is evaluated. Then, a cantilever beam model is used to analyze the thin material machining failure problem, and a minimum layer thickness model is presented. Third, a combined layer thickness & tool size model is constructed based on the machining tool deflection under cutting forces. This rapid pattern manufacturing process and related software has been implemented, and experimental data is presented to illustrate the efficacy of this system and its process planning methods

Statistical Approach to the Characterization and Recognition of Human Gaits

This thesis addresses the final portion of a complete process for human gait recognition. The thesis takes as input information that has been generated from videotaping walking individuals and converting their gaits into numerical data that measures the locations of various points on the body through time. Beginning with this data, this thesis uses a variety of mathematical and statistical methods to create identifying signatures for each individual and identify them on the basis of that signature. The end goal is to achieve under controlled laboratory conditions human gait recognition, an identification method which does not require contact or cooperation with the individual and which can be done unobserved from a distance. Various mathematical models such as the construction of classifiers utilizing Minimum Euclidean Distance, Minimum Mahalanobis Distance and Quadratic Discriminant Functions are employed on both static and dynamic characteristics in order to fully analyze gait data for the purposes of identification. This thesis starts with previously generated numerical data from a videotaped sequence of images of a subject walking across a room that contains the positions through time of a wide variety of different markers on the individual’s body. A MatLab program is initially written to convert the data into a usable format. A variety of mathematical techniques are then employed to generate several classifiers of an individual from a small set of gaits that can be used to identify their gait in any data set

Dynamic optimization based reactive power planning for improving short-term voltage performance

Short term voltage stability in the form of delayed voltage recovery (FIDVR) poses a significant threat to system stability and reliability. This work examines the voltage instability issue in a power system with dense concentration of induction motor loads and applies dynamic VAR injection as a counter-measure to ensure short term voltage stability following a large disturbance. The dynamic behavior of motor loads, such as decelerating and stalling, is considered as the major cause of FIDVR incidents especially during summer peak load conditions in areas where low inertia single-phase air conditioning (A/C) motors comprise a significant portion of the load. If system dynamics are not taken into account properly, the proposed control solution may be an expensive over design or an under design which is not capable of mitigating FIDVR problems completely. This work aims to provide a comprehensive dynamic VAR planning strategy for handling short term voltage stability problems by proper consideration of system dynamics, multiple contingencies, multiple scenarios and operating conditions. In addition, this approach aims to provide valuable system insights such as behavior of different contingencies and dynamic voltage control areas. Contingencies are clustered together according to their behavioral similarity with respect to voltage performance using an entropy based metric called Kullback-Liebler (KL) measure. Using the information of contingency clusters, a new concept called dynamic voltage control areas is derived. The concept of dynamic voltage control area will address the importance of the location of dynamic reactive reserves. Control vector parameterization (CVP), a dynamic optimization based approach is used to identify the optimal locations and amount of dynamic VARs required to mitigate short term voltage problems. The main idea of CVP approach is to solve the system dynamics separately and utilize the system dynamics results in the constraints evaluation during optimization routine. Also this method is applicable to large scale systems because of the utilization of commercial power system and large scale optimization solvers. Simulations have been carried out on modified IEEE 162 bus system to show the working of contingency clustering, dynamic voltage control area identification and CVP method for single contingency case. The CVP method has also been tested on a large scale realistic power system to show the scalability of the proposed approach

Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms

The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

Accelerated volumetric reconstruction from uncalibrated camera views

While both work with images, computer graphics and computer vision are inverse problems. Computer graphics starts traditionally with input geometric models and produces image sequences. Computer vision starts with input image sequences and produces geometric models. In the last few years, there has been a convergence of research to bridge the gap between the two fields. This convergence has produced a new field called Image-based Rendering and Modeling (IBMR). IBMR represents the effort of using the geometric information recovered from real images to generate new images with the hope that the synthesized ones appear photorealistic, as well as reducing the time spent on model creation. In this dissertation, the capturing, geometric and photometric aspects of an IBMR system are studied. A versatile framework was developed that enables the reconstruction of scenes from images acquired with a handheld digital camera. The proposed system targets applications in areas such as Computer Gaming and Virtual Reality, from a lowcost perspective. In the spirit of IBMR, the human operator is allowed to provide the high-level information, while underlying algorithms are used to perform low-level computational work. Conforming to the latest architecture trends, we propose a streaming voxel carving method, allowing a fast GPU-based processing on commodity hardware