Process compensated resonance testing (PCRT) inversion for material characterization and digital twin calibration

Mayes, Alex; Heffernan, Julieanne; Jauriqui, Leanne; Livings, Richard; Biedermann, Eric; Aldrin, John C.; Goodlet, Brent; Mazdiyasni, Siamack

doi:10.1063/1.5099723

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…AI-driven digital process twins are envisioned to learn and interpret implicit correlations between manufacturing processes and material/process/environmental parameters from an aggregation of (heterogeneous) data with the objective of optimizing process development, production ramp-up, and quality assurance cycle. From an engineering implementation perspective, we have noted that despite the significance of algorithms and models, novel sensor technologies [167,[213][214][215][216][217] and networked digital process chains [218][219][220][221][222] should not be neglected, as they are essential pillars for constructing DTs and can considerably influence the effectiveness and efficiency of their development and deployment in practice. Figure 5 shows an example of a DT dynamically mapping the manufacturing process of an aerospace part and the data sources involved from the contextualized CAD-CAM-CNC-CAQ process chain.…”

Section: Interim Summarymentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

158

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Interim Summarymentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

158

View full text Add to dashboard Cite

show abstract

“…Through the use of various methods (such as X-CT, ultrasound, penetrant testing, microscopic sectioning, operating performance, and operator feedback), the blades were initially labeled into the groups Good or Bad. Through vibrational testing, 6–8 the relevant resonance frequencies, and their associated Q-factors, and amplitudes in the frequency range of [ 3,39 kHz] were obtained. In this study, 15 extracted resonance frequencies are used in MTS, two-stage MCS, and IMCS.…”

Section: Case Study Analysismentioning

confidence: 99%

“…The Mahalanobis–Taguchi system (MTS) is a well-known classification algorithm for pattern recognition of multivariate system classification and prediction in the application of vibrational testing. 7,8 It is based on the Mahalanobis distance (MD) concept proposed by PC Mahalanobis 9 in 1936 and used for the analysis of multivariate anomaly detection. G Taguchi 10–12 combined the concept of MD with robust design theory to develop MTS methods for system dimension reduction analysis and diagnosis/prediction.…”

Section: Introductionmentioning

confidence: 99%

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

Cheng

Yaghoubi

Paepegem

et al. 2020

Structural Health Monitoring

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The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

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A classification proposal of digital twin applications in the safety domain

Agnusdei

Elia

Gnoni

2021

Computers & Industrial Engineering

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Process compensated resonance testing (PCRT) inversion for material characterization and digital twin calibration

Cited by 7 publications

References 15 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

A classification proposal of digital twin applications in the safety domain

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