Framework for process improvement in manufacturing of metal packaging

Thesis (MEng)--Stellenbosch University, 2022.ENGLISH ABSTRACT: Due to increased competitiveness in the packaging industry, process improvement is important to give businesses an edge over their competition. This thesis represents a study of the application of machine learning for process improvement in metal can manufacturing. A five step process improve ment framework based on the Six Sigma process improvement methodology and the CRISP-DM data science framework was developed. The framework consisted of different steps that included steps used in the Six Sigma process improvement methodologies as well as steps used in data science processes.The five steps were; Define, Understand, Model, Evaluate and Deploy (DUMED). The DUMED framework was used in a case study that predicted the axial load resistance of 2-piece metal food cans during the manufacturing process. The objective is to understand how axial load resistance relates to other factors in the process with the outcome that any changes made in the process will still deliver cans with suitable axial load resistance. A predictive model on axial load resistance will give enhanced capability to control axial load resistance, and will lead to less rejections and therefore less waste. A predictive model on axial load resistance can also supply valuable information on the possible viability for light weighting of material, which will have a decreased cost of raw material as a result and therefore hold financial benefit for the manufacturer. Various data science and machine learning principles were applied during the study related to data understanding, data assessing, data preparation, data modelling and model assessing. The framework was successfully applied in the case study, with the exception of the fifth step, deployment. The deployment phase will be dependent on further improvement of the predictive model. Machine learning was successfully used in the case study to develop a predictive model; the axial load resistance could be predicted within 2.3% of the actual values. The best results were obtained from using feature selected data obtained from a random forest feature selection algorithm that was modelled by using a gradient boost ensemble regression model. Machine learning was successfully applied to a metal package manufacturing line to predict quality characteristics of the final product and possibly bring about process improvement.AFRIKAANSE OPSOMMING: As gevolg van die toenemende kompetisie in die verpakkings industrie is proses verbetering belangrik om besighede ’n voorsprong oor hulle kompetisie te gee. Hierdie tesis is ’n studie van die gebruik van masjienleer vir proses verbetering in metaal blik vervaardiging. ’n Vyf stap proses verbeterings raamwerk wat gebaseer was op die Ses Sigma proses verbeterings metodologie an die CRISP-DM data wetenskap raamwerk was ontwikkel. Die vyf stappe was; definieer, verstaan, modeleer, eval ueer, en ontplooi (DUMED, na aanleiding van die engelse akroniem). Die DUMED raamwerk was gebruik vir ’n gevallestudie wat die aksiale ladings weerstand van 2-stuk metaal kos blikke voorspel gedurende die vervaardigings proses. Verskeie data wetenskap en masjienleer beginsels was toegepas gedurende die studie relevant tot die verstaan van die data, assessering van die data, voorbereiding van die data, modelering van die data en die assessering van die data modelle. Die raamwerk was suk sesvol toegepas vir die gevallestudie, behalwe vir die vyfde stap, naamlik die ontplooing. Die ontploo ings fase sal afhanklik wees van verdere verbeteringe op die voorspellende data model. Masjienleer was suksesvol gebruik in die gevallestudie om ’n voorspellende model te ontwikkel; die aksiale lad ings weerstand kon voorspel word tot binne 2.3% van die werklike waardes. Die beste resultaat was verkry deur die ’gradient boost’ masjienleer algoritme toe te pas op ’random forest feature selected’ data. Masjienleer was suksesvol toegepas op ’n metaal verpakkings vervaardigings lyn om kwaliteits eienskappe op die finale produk te voorspel en so moontlikke proses verbetering te bewerkstellig.Master

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

La localización y mapeado simultáneo a partir de una sola cámara en movimiento se conoce como Monocular SLAM. En esta tesis se aborda este problema con cámaras de bajo coste cuyo principal reto consiste en ser robustos al ruido, blurring y otros artefactos que afectan a la imagen. La aproximación al problema es discreta, utilizando solo puntos de la imagen significativos para localizar la cámara y mapear el entorno. La principal contribución es una simplificación del grafo de poses que permite mejorar la precisión en las escenas más habituales, evaluada de forma exhaustiva en 4 datasets. Los resultados del mapeado permiten obtener una reconstrucción 3D de la escena que puede ser utilizada en arquitectura y construcción para Modelar la Información del Edificio (BIM). En la segunda parte de la tesis proponemos incorporar dicha información en un sistema de visualización avanzada usando WebGL que ayude a simplificar la implantación de la metodología BIM.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information