A Multiclassifier Approach for Drill Wear Prediction

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach

Mining association rules for the quality improvement of the production process

Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment’s results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes

Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Robots and tools for remodeling bone

The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery

Automated computer vision system for real-time drilling cuttings monitoring

In rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin

Sharp-Tailed Grouse Nest Survival and Nest Predator Habitat Use in North Dakota’s Bakken Oil Field

Recent advancements in extraction technologies have resulted in rapid increases of gas and oil development across the United States and specifically in western North Dakota. This expansion of energy development has unknown influences on local wildlife populations and the ecological interactions within and among species. Our objectives for this study were to evaluate nest success and nest predator dynamics of sharp-tailed grouse (Tympanuchus phasianellus) in two study sites that represented areas of high and low energy development intensities in North Dakota. During the summers of 2012 and 2013, we monitored 163 grouse nests using radio telemetry. Of these, 90 nests also were monitored using miniature cameras to accurately determine nest fates and identify nest predators. We simultaneously conducted predator surveys using camera scent stations and occupancy modeling to estimate nest predator occurrence at each site. American badgers (Taxidea taxus) and striped skunks (Mephitis mephitis) were the primary nest predators, accounting for 56.7% of all video recorded nest depredations. Nests in our high intensity gas and oil area were 1.95 times more likely to succeed compared to our minimal intensity area. Camera monitored nests were 2.03 times more likely to succeed than non-camera monitored nests. Occupancy of mammalian nest predators was 6.9 times more likely in our study area of minimal gas and oil intensity compared to the high intensity area. Although only a correlative study, our results suggest energy development may alter the predator community, thereby increasing nest success for sharp-tailed grouse in areas of intense development, while adjacent areas may have increased predator occurrence and reduced nest success. Our study illustrates the potential influences of energy development on the nest predator—prey dynamics of sharp-tailed grouse in western North Dakota and the complexity of evaluating such impacts on wildlife

Modelling laser milling of microcavities for the manufacturing of DES with ensembles

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.This work was partially funded through Grants fromthe IREBID Project (FP7-PEOPLE-2009-IRSES- 247476) of the European Commission and Projects TIN2011- 24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and Competitivenes

Drill monitor with strata strength classification in near-real time

"The process of drilling and bolting the roof is currently one of the most dangerous jobs in underground mining, resulting in about 1,000 accidents with injuries each year in the United States. Researchers from the Spokane Research Laboratory of the National Institute for Occupational Safety and Health are studying the use of a drill monitoring system to estimate the strength of successive layers of rock and assess the integrity of a mine roof so that roof drill operators can be warned when a weak layer is being drilled. Measurements taken during the drilling can be converted to suitably scaled features so that a neural network can classify mine roof strata in terms of relative strength. The feasibility of this concept has been demonstrated in the laboratory. The research project was undertaken in order to increase the safety of underground miners, especially those involved in roof bolting., The system should be applicable to the mobile drills used in underground mining and would likely find wider application as well." - NIOSHTIC-2Includes bibliographical references (p. 14

IODP Expedition 325: The Great Barrier Reefs Reveal Past Sea-Level, Climate and Environmental Changes Since the Last Ice Age

The timing and courses of deglaciations are key components in understanding the global climate system. Cyclic changes in global climate have occurred, with growth and decay of high latitude ice sheets, for the last two million years. It is believed that these fluctuations are mainly controlled by periodic changes to incoming solar radiation due to the changes in Earth’s orbit around the sun. However, not all climate variations can be explained by this process, and there is the growing awareness of the important role of internalclimate feedback mechanisms. Understanding the nature of these feedbacks with regard to the timing of abrupt global sea-level and climate changes is of prime importance. The tropical ocean is one of the major components of the feedback system, and hence reconstructions of temporal variations in sea-surface conditions will greatly improve our understanding of the climate system. The Integrated Ocean Drilling Program (IODP) Expedition 325 drilled 34 holes across 17 sites in the Great Barrier Reef, Australia to recoverfossil coral reef deposits. The main aim of the expedition was to understand the environmental changes that occurred during the last ice age and subsequent deglaciation, and more specifically (1) establish the course of sea-level change, (2) reconstruct the oceanographic conditions, and (3) determine the response of the reef to these changes. We recovered coral reef deposits from water depths down to 126 m that ranged in age from 9,000 years to older than 30,000 years ago. Given that the interval of the dated materials covers several paleoclimatologically important events, includingthe Last Glacial Maximum, we expect that ongoing scientific analyses will fulfill the objectives of the expedition

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201