Asymptotic defectiveness of manufacturing plants: an estimate based on process learning curves

The paper describes a method for a preliminary estimation of asymptotic defectiveness of a manufacturing plant based on the prediction of its learning curve estimated during a p-chart setting up. The proposed approach provides process managers with the possibility of estimating the asymptotic variability of the process and the period of revision of p-chart control limits. An application of the method is also provided

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Empirical Mode Decomposition of Pressure Signal for Health Condition Monitoring in Waterjet Cutting

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach

Statistical Quality Control of a Production Process of Invisible Zippers

The present work exhibits the development and implementation of an innovative sta- tistical control plan applied to the production process of invisible zippers, within the context of a project developed by the NOVA School of Science and Technology for a zipper producer. The plan focused on two main axes: reevaluating currently applied sampling plans and iden- tifying critical characteristics in the various stages of the process, with the development of a proposal for control charts for each of the stages and implementation of the respective charts. An important part of the plan was the implementation of a design of experiments to optimize critical processes. Consequently, an integrated approach was implemented to define and solve the prob- lem. At first, a complete definition and description of the process was executed through a visual representation with flowcharts. Then, critical points of the process were identified, which led to a preliminary implementation of control charts, planification of a design of ex- periments, and execution of several hypothesis tests. Even though, as of the redaction of this study, no improvement on the process was achieved, several crucial conclusions were reached over its behavior following the implemen- tation of the statistical tools. Some important conclusions were the out-of-control state of the process on some important characteristics, and strong presence of internal variability in the process. As a result, a design of experiments was considered the best approach for improve- ment, and its full planification has been achieved, as it is currently being performed. As for the sampling plans, a necessity to reduce end-of-line inspections was identified and is expected to be enabled by the improvements arising from the design of experiments. On the other hand, the reception sampling plan was identified as insufficient, and is to be reviewed.O presente trabalho expõe o desenvolvimento e implementação de um plano de con- trolo estatístico inovador aplicado ao processo de produção de fechos invisíveis, no contexto de um projeto desenvolvido pela Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para um produtor de fechos de correr. O plano centrou-se em dois eixos principais: a reavaliação dos planos de amostragem atualmente aplicados e identificação de característi- cas críticas nas várias fases do processo, com o desenvolvimento de uma proposta de cartas de controlo para cada uma das fases e implementação das respetivas cartas. Uma parte im- portante do plano foi a implementação de um desenho de experiências para a otimização de processos críticos. Consequentemente, foi implementada uma abordagem integrada para definir e resolver o problema. No início, foi realizada uma definição e descrição completa do processo através de uma representação visual com fluxogramas. De seguida, foram identificados pontos críti- cos do processo, o que levou à implementação preliminar de cartas de controlo, planificação de um desenho de experiências e execução de vários testes de hipóteses. Apesar de, à data de redação deste estudo, não se ter alcançado uma melhoria do pro- cesso, alcançaram-se várias conclusões cruciais sobre o seu comportamento. Algumas conclu- sões importantes foram o estado fora de controlo do processo em certas características impor- tantes, e a forte presença de variabilidade interna no processo. Como resultado, o desenho de experiências foi considerado a melhor abordagem para a sua melhoria, e a sua planificação completa foi efetuada, sendo que as experiências se encontram de momento a decorrer. Quanto aos planos de amostragem, foi identificada a necessidade de reduzir as inspe- ções de fim de linha. Por outro lado, o plano de amostragem de receção foi identificado como insuficiente, e deverá ser revisto

Improving Material Utilisation in E2E Upstream Supply Chain Operations: A Multiple Case Study

The increasing cost of manufacturing and the constant need for organisations to remain competitive and profitable is garnering unprecedented attention of supply chain practitioners and academia. Several approaches are being employed in minimising raw material losses within supply chain networks. The study of effective utilisation of raw materials are therefore of great importance to manufacturing organisations seeking to increase the efficiency of their operations while reducing material related losses. By improving the utilisation of raw material, huge cost savings is achievable within the supply chain operations that are focused on the radical reduction of raw material wastes during its transportation and transformation processes. This study makes uses a multiple case approach to investigate MU in the upstream supply chain operations, and utilises a mixed research method to explore the process approaches utilised by the case organisations in minimising MU losses and improving their manufacturing system.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Optimal experimental design for mathematical models of haematopoiesis.

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters

Parts and materials application review for space systems

Parts and materials application review for project management of space systems engineerin

A comparison study of distribution-free multivariate SPC methods for multimode data

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data. Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications