Modeling of a Thermal Spraying Process by Gaussian Chain Graphs

Rudak, Nikolaus; Kuhnt, Sonja; Riccomagno, Eva

doi:10.1080/16843703.2014.11673327

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…In ref. [ 97 ] a thermal spraying process is modeled by Gaussian chain graph models. The problem is that coating quality is difficult to reproduce due to uncontrollable day effects.…”

Section: Discussion and Future Developmentsmentioning

confidence: 99%

Challenges and Opportunities in Simulations and Computer Experiments in Industrial Statistics: An Industry 4.0 Perspective

Kenett¹,

Vicario

2021

Advcd Theory and Sims

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“…In ref. [ 97 ] a thermal spraying process is modeled by Gaussian chain graph models. The problem is that coating quality is difficult to reproduce due to uncontrollable day effects.…”

Section: Discussion and Future Developmentsmentioning

confidence: 99%

Challenges and Opportunities in Simulations and Computer Experiments in Industrial Statistics: An Industry 4.0 Perspective

Kenett¹,

Vicario

2021

Advcd Theory and Sims

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“…For an introduction to FEM, see Zienkiewicz. 35 Digital twins have been used to simulate the Rockwell C hardness test for assessing the resistance of materials 36 In such tests an indenter is pressed into the material and the depth of penetration is measured. After performing extensive numerical experiments, a few experimental runs were conducted in the lab to validate the digital twin model.…”

Section: Other Examples Of Digital Twinsmentioning

confidence: 99%

The digital twin in Industry 4.0: A wide‐angle perspective

Kenett

Bortman

2021

Quality & Reliability Eng

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The move towards advanced manufacturing and Industry 4.0 is fed by increased demand for speeding up innovation, increasing flexibility, improving maintenance, and becoming more customized while saving on the total cost of operations. This is accompanied by increased dependence on virtual product and process development, data‐driven processes, and product knowledge. Other characteristics, affecting modern design, include big data intelligence in product, process, and maintenance. New technologies that empower data analytics include added manufacturing, flexible manufacturing, robotics, sensor technology, smart value/supply chains, and industrial information backbones. This paper is about surrogate models, also called digital twins, that provide an important complementary capacity to physical assets. Digital twins capture past, present, and predicted behavior of physical assets. Digital twin models are updated periodically to represent the current state of physical assets. This distinguishes digital twins from conventional simulations in that sensor data can continuously feed them. The type of curated information on the state of physical asset's history depends on how digital twins are used. For example, if a digital twin is used for fault classification, the history captured is operational data from equipment in healthy and faulty states. We provide here a review of digital twins, with an emphasis on Industry 4.0 applications.

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“…Often, model elicitation in point 3 and fault probability identification in point 4 are by error‐and‐try and by adjusting the model following expert advice. Once sufficient data are available, expert knowledge in point 2 could be substituted by a number of softwares; although we do not advocate a full substitution, for a comparative study of available software, we refer to the work of Rudak et al …”

Section: Case Studymentioning

confidence: 99%

Modelling of Fault Detection and Diagnostics for Hybrid Bus Using Chain Graph Models

Flaccadoro

Cervellera

Bosia

et al. 2014

Quality & Reliability Eng

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Graphical models are statistical models supported on a graph structure: nodes represent random variables, and missing edges represent probabilistic relationship of conditional independence. This makes them suited to model the behavior of complex systems that are difficult to model through mathematical equations. In this work, this possibility is exploited in a context of diagnostics and fault detection. Specifically, the fault detection problem is reduced to the evaluation of a conditional probability. The relevant conditional distribution is derived from the analysis of a suitable graphical model taking advantage of the so‐called Markov properties.As a case study, we consider diagnostics of a hybrid bus, characterized by the combined use of a diesel engine and an electrical engine. The aim of the study is to verify the correct operation of the electrical system, in particular the status of the battery. Copyright © 2014 John Wiley & Sons, Ltd.

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Modeling of a Thermal Spraying Process by Gaussian Chain Graphs

Cited by 6 publications

References 3 publications

Challenges and Opportunities in Simulations and Computer Experiments in Industrial Statistics: An Industry 4.0 Perspective

Challenges and Opportunities in Simulations and Computer Experiments in Industrial Statistics: An Industry 4.0 Perspective

The digital twin in Industry 4.0: A wide‐angle perspective

Modelling of Fault Detection and Diagnostics for Hybrid Bus Using Chain Graph Models

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