Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms

Balakrishnan, P.; Babu, Kalivaradhan Ramesh; Naiju, C.D.; Madiajagan, M.

doi:10.4271/2019-28-0159

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Ref. [19] presents a case study on digital twin applications in automobile fault prediction, utilizing real-time sensor data for future failure predictions. Ref.…”

Section: Literature Review On the Digital Twin Technology And Its App...mentioning

confidence: 99%

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

Khdoudi,

Masrour,

El Hassani

et al. 2024

Systems

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In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector.

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“…Ref. [19] presents a case study on digital twin applications in automobile fault prediction, utilizing real-time sensor data for future failure predictions. Ref.…”

Section: Literature Review On the Digital Twin Technology And Its App...mentioning

confidence: 99%

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

Khdoudi,

Masrour,

El Hassani

et al. 2024

Systems

View full text Add to dashboard Cite

show abstract

“…However, with the availability of DT tools enabling a continuous single flow of information along the product lifecycle, obtaining accurate geometrical deviations of compliant parts could become feasible. In particular, DT of product design could be built to send real-time specifications information and variation information to support the general assembly planning (Franciosa et al, 2020;Polini and Corrado, 2020;Sierla et al, 2018), including in automotive industry (Balakrishnan et al, 2019). Meanwhile, others attempted to use selective assembly techniqueproduct simulation (Computer-Aided Tolerancing tool) to find the optimal combination of individual parts to assemble the best quality final product, in sheet metal assembly (Rezaei Aderiani et al, 2019).…”

Section: Cluster 9: Dt In Product Assembly Processmentioning

confidence: 99%

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen

Duong

Nguyen

et al. 2022

International Journal of Production Economics

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“…Monte Carlo simulation can be performed to generate fleet data (Zaccaria et al, 2018). Both the signature-based technique and the machine learning approach can be utilized for data processing; the machine learning approach includes the random forest regressor (RFR), support vector regressor (SVR), gradient boosting regressor (GBR) and artificial neural network (ANN) (Zaccaria et al, 2018;Balakrishnan, 2019).…”

Section: Dt In Product Lifecycle Phasesmentioning

confidence: 99%

Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges

Zhang

Deng

et al. 2021

JIMSE

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Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

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Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms

Cited by 6 publications

References 29 publications

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges

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