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

Kenett, Ron S.; Vicario, Grazia

doi:10.1002/adts.202000254

Cited by 10 publications

(14 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tao et al 26 and Kenett et al 27 pointed out that a digital twin consists of five components: the physical part, virtual part, connection, data, and service. The physical part consists of various functional subsystems and sensory devices, and the virtual part comprises a geometric model, physical properties assigned, and a behavioral model as well as operating rules.…”

Section: Existing Studies On Digital Twinmentioning

confidence: 99%

Virtual qualification using digital twin for product development: A case study on liquid crystal display

Choi,

Ahn,

Yoo

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

A virtual qualification (VQ) model is proposed using a digital twin not only for verifying electrical properties of a liquid crystal display (LCD) but also for assessing the effect of environmental stresses. The digital twin is developed to reflect product specifications and distributions of manufacturing process data. The VQ model utilizes the digital twin to infer and verify the changes in electrical characteristic values in the internal circuit under a high‐temperature operating test. Additionally, a procedure is proposed for developing digital twins, and its effectiveness is demonstrated based on a case study of an LCD. The developed digital twin may also be used for similar LCD models for design verification.

show abstract

Section: Existing Studies On Digital Twinmentioning

confidence: 99%

Virtual qualification using digital twin for product development: A case study on liquid crystal display

Choi,

Ahn,

Yoo

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…[8] Simulations and computer DOI: 10.1002/adts.202100459 experiments play a growing role, such as Industry 4.0, which applies in a wide range of process engineering, product design, and analytic disciplines. [9] However, as a black box model stacked with nonlinear learners, ANNs are too complex to be traceable by humans. There is a growing demand for interpretability and explanation methods to make the ANNs transparent.…”

Section: Introductionmentioning

confidence: 99%

Interpretability of Neural Networks with Probability Density Functions

Pan

Pedrycz

Cui

et al. 2022

Advcd Theory and Sims

View full text Add to dashboard Cite

It is an interesting topic to interpret artificial neural networks (ANNs) by considering some change various approaches. This paper explores the relationship between the input and output units of the simplest ANN, a single layer perceptron for the binary classification problem, from the probability point of view. If the feature variables of datasets follow independent normal distribution and outputs are activated by sigmoid function or smooth Relu function, we advocate that the probability density function (pdf) of the output variable is an exponential family distribution. Furthermore, by introducing an intermediate variable, the pdf of the output variable can be written as a linear combination of three normal distributions with same spread but different centers. Based on these results, the probability of the predicted class label can be written as a standard normal cumulative distribution function (cdf). The originality of this paper comes with interesting theoretical results to provide ANNs with a new description of the relationship between input variables to output variables, which can enable ANNs to be understood from a new perspective. Extensive experiments based on one artificial synthesized dataset and ten real‐world benchmark datasets validate the reasonability of those results.

show abstract

“…Digital twins complement or substitute physical experiments with software‐based simulation experiments 21–24 .A digital twin can be used to run experiments where factors are parametrized as a subset of the code's inputs (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Simulating the behavior of a system, with computer software requires validation and calibration. 19,20 Digital twins complement or substitute physical experiments with software-based simulation experiments [21][22][23][24] .A digital twin can be used to run experiments where factors are parametrized as a subset of the code's inputs (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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

Kenett

Bortman

2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 10 publications

References 84 publications

Virtual qualification using digital twin for product development: A case study on liquid crystal display

Virtual qualification using digital twin for product development: A case study on liquid crystal display

Interpretability of Neural Networks with Probability Density Functions

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

Contact Info

Product

Resources

About