Bayesian networks in additive manufacturing and reliability engineering

A Bayesian network (BN) is a powerful tool to represent the quantitative and qualitative features of a system in an intuitive yet sophisticated manner. The qualitative aspect is represented with a directed acyclic graph (DAG), depicting dependency relations between the random variables of the system. In a DAG, the variables of the system are shown with a set of nodes and the dependencies between them are shown with a directed edge. A DAG in the Bayesian network can be a causal graph under certain circumstances. The quantitative aspect is the local conditional probabilities associated with each variable, which is a factorization of the joint probability distribution of the variables in the system based on the dependency relation represented in the DAG. In this study, the benefits of using BNs in reliability engineering and additive manufacturing is investigated. In the case of reliability engineering, there are several methods to create predictive models for reliability features of a system. Predicting the possibility and the time of a possible failure is one of the important tasks in the reliability engineering principle. The quality of the corrective maintenance after each failure is affecting consecutive failure times. If a maintenance task after each failure involves replacing all the components of an equipment, called perfect maintenance, it is considered that the equipment is restored to an “as good as new” (AGAN) condition, and based on that, the consecutive failure times are considered independent. Not only in most of the cases the maintenance is not perfect, but the environment of the equipment and the usage patterns have a significant effect on the consecutive failure times. In this study, this effect is investigated by using Bayesian network structural learning algorithms to learn a BN based on the failure data of an industrial water pump. In additive manufacturing (AM) field, manufacturing systems are normally a complex combination of multiple components. This complex nature and the associated uncertainties in design and manufacturing parameters in additive manufacturing promotes the need for models that can handle uncertainties and are efficient in calculations. Moreover, the lack of AM knowledge in practitioners is one of the main obstacles for democratizing it. In this study, a method is developed for creating Bayesian network models for AM systems that includes experts’ and domain knowledge. To form the structure of the model, causal graphs obtained through dimensional analysis conceptual modeling (DACM) framework is used as the DAG for a Bayesian network after some modifications. DACM is a framework for extracting the causal graph and the governing equations between the variables of a complex system. The experts’ knowledge is extracted through a probability assessment process, called the analytical hierarchy process (AHP) and encoded into local probability tables associated with the independent variables of the model. To complete the model, a sampling technique is used along with the governing equations between the intermediate and output variables to obtain the rest of the probability tables. Such models can be used in many use cases, namely domain knowledge representation, defect prognosis and diagnosis and design space exploration. The qualitative aspect of the model is obtained from the physical phenomena in the system and the quantitative aspect is obtained from the experts’ knowledge, therefore the model can interactively represent the domain and the experts’ knowledge. In prognosis tasks, the probability distribution for the values that an output variable can take is calculated based on the values chosen for the input variables. In diagnosis tasks, the designer can investigate the reason for having a specific value in an output variable among the inputs. Finally, the model can be used to perform design space exploration. The model reduces the design space into a discretized and interactive Bayesian network space which is very convenient for design space exploration

Optimal Kanban Number: An Integrated Lean and Simulation Modelling Approach

Kanban is credited as a major means to controlling the inventory within a manufacturing system. Determining the optimum number of Kanban is of great interest for manufacturing industries. To fulfill this aim, an integrated modelling approach using discrete-event simulation technique and Kanban Lean tool is developed for a pull system ensuring an optimum Kanban number. This research has developed a base-case simulation model which was statistically validated using ANOVA. Initial Kanban number obtained from the mathematical model of Toyota motor company is used to obtain initial results. A Kanban integrated simulation model is developed that employed the idea of pull system that required the arrival of a customer for a product and Kanban pair to proceed through the production steps. The Kanban-Simulation integrated model is further used to test the effect of different Kanban numbers to obtain the best value of Kanban which is selected as 275. This approach has been applied on a case company involved in the manufacturing of agricultural and construction metal hand tools. The optimum Kanban number is selected by simulating the model about three performance indicators: customer waiting time, weekly throughput, and Work-in-progress. The analysis of the results obtained from the proposed integrated Kanban-simulation model showed a 76.7% reduction in the inventory level. The integrated Kanban-simulation model has also given a minimum customer waiting time of 0.84 Hrs. and a maximum throughput value of 737 Pcs of shovels. The integrated Kanban-simulation model is useful for manufacturing industries working to avoid overproduction waste and greatly reduce inventory costs

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time

Lifetime multi-objective optimization of maintenance of existing steel structures

In this paper, the lifetime performance of deteriorating structures, defined by their time dependent condition index and reliability index, is analyzed. The effect of preventive and essential maintenance actions on performance and cost in predicted, and the optimal times of application of preventive and essential maintenance actions are found. Due to significant uncertainty in the initial performance, effects of deterioration and of maintenance actions, as well as, times of application and cost of maintenance actions, the analysis is performed in a probabilistic framework. The reduction in performance due to deterioration is simulated using an extension of the model proposed by Frangopol (1998). The probabilistic condition index, reliability index, and cumulative cost profiles are computed using Latin Hypercube simulation. Optimization of times of application is performed using genetic algorithms. Results show the significant importance of preventive maintenance actions in reducing the lifetime cost of existing structures, but also their fundamental role of essential maintenance action in keeping structures safe and serviceable during the entire lifetime

How Infectious is Your Twitter Feed? Disease Modeling Applied to the Dynamics of Twitter

This study aims to use compartmental disease-models to explore Twitter dynamics. Applying an epidemiology model to Twitter tweets can give deeper insights into the factors that make a tweet go viral. In addition, this study explored the differences between a stochastic and a dynamic compartmental model. This research connects the world of diseases with the internet and explored if a disease model will accurately model Twitter dynamics. We found that stochastic models were better at fitting to smaller populations of data than dynamic models were. Dynamic models ended up predicting larger populations better. Furthermore, we found that although a topic is popular does not mean that it is infectious. This study was able to show that disease modeling is able to accurately predict Twitter dynamics

Cauliflower Mosaic Virus TAV, a Plant Virus Protein That Functions like Ribonuclease H1 and is Cytotoxic to Glioma Cells

Recent comparisons between plant and animal viruses reveal many common principles that underlie how all viruses express their genetic material, amplify their genomes, and link virion assembly with replication. Cauliflower mosaic virus (CaMV) is not infectious for human beings. Here, we show that CaMV transactivator/viroplasmin protein (TAV) shares sequence similarity with and behaves like the human ribonuclease H1 (RNase H1) in reducing DNA/RNA hybrids detected with S9.6 antibody in HEK293T cells. We showed that TAV is clearly expressed in the cytosol and in the nuclei of transiently transfected human cells, similar to its distribution in plants. TAV also showed remarkable cytotoxic effects in U251 human glioma cells in vitro. *ese characteristics pave the way for future analysis on the use of the plant virus protein TAV, as an alternative to human RNAse H1 during gene therapy in human cells

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

We study reliability equivalence factors of a system of independent and identical components with exponentiated Weibull lifetimes. The system has n subsystems connected in parallel and subsystem i has mi components connected in series, i=1,…,n. We consider improving the reliability of the system by (a) a reduction method and (b) several duplication methods: (i) hot duplication; (ii) cold duplication with perfect switching; (iii) cold duplication with imperfect switching. We compute two types of reliability equivalence factors: survival equivalence factors and mean equivalence factors. Although our methods adapt to allow for general lifetime models, we use the exponentiated Weibull distribution because it is flexible and enables comparisons with other reliability equivalence studies. The example we present demonstrates the potential for applying these methods to address specific questions that arise when attempting to improve the reliability of simple systems or simple configurations of possibly complex subsystems in many diverse applications

Three-dimensional nanomagnetism

Magnetic nanostructures are being developed for use in many aspects of our daily life, spanning areas such as data storage, sensing and biomedicine. Whereas patterned nanomagnets are traditionally two-dimensional planar structures, recent work is expanding nanomagnetism into three dimensions; a move triggered by the advance of unconventional synthesis methods and the discovery of new magnetic effects. In three-dimensional nanomagnets more complex magnetic configurations become possible, many with unprecedented properties. Here we review the creation of these structures and their implications for the emergence of new physics, the development of instrumentation and computational methods, and exploitation in numerous applications

Experimental and numerical analysis of the martensitic transformation in AISI 304 steel sheets subjected to perforation by conical and hemispherical projectiles

In this work, an experimental and numerical analysis of the martensitic transformation in AISI 304 steel sheets subjected to perforation by conical and hemispherical projectiles is conducted. Experiments are performed using a pneumatic gas gun for with the impact velocities in the range of 35 m/s < V-0 < 200 m/s. Two target thicknesses are examined, t(1) = 0.5 mm and t(2) = 1.0 mm. The experimental setup enabled the determination of the impact velocity, the residual velocity and the failure mode of the steel sheets. The effect of the projectile nose shape on the target's capacity for energy absorption is evaluated. Moreover, martensite is detected in all the impacted samples, and the role played by the projectile nose shape on the transformation is highlighted. A three-dimensional model is developed in ABAQUS/Explicit to simulate the perforation tests. The material is defined via the constitutive model developed by Zaera et al. (2012) to describe the strain-induced martensitic transformation occurring in metastable austenitic steels at high strain rates. The finite element results are compared with the experimental evidence, and satisfactory matching is observed over the entire range of impact velocities tested and for both projectile configurations and target thicknesses considered. The numerical model succeeds in describing the perforation mechanisms associated with each projectile-target configuration analyzed. The roles played by impact velocity, target thickness and projectile nose shape on the martensitic transformation are properly captured.The researchers of the University Carlos III of Madrid are in debted to the Comunidad Autónoma de Madrid (Project CCG10 UC3M/DPI 5596) and to the Ministerio de Ciencia e Innovación de España (DPI2011 24068) for the financial support received which allowed conducting part of this work