Manufacturing resilience and agility through processes digital twin: design and testing applied in the LPBF case

Papacharalampopoulos, Alexios; Michail, Christos K.; Stavropoulos, Panagiotis

doi:10.1016/j.procir.2021.10.026

Cited by 24 publications

(8 citation statements)

References 25 publications

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“…[13] delves into data ownership in industry, identifying reliable data sources for metal-based product manufacturing, while Refs. [10,14] explore the integration of various models into a digital twin for manufacturing, particularly LPBF, to enhance adaptability, optimization, and uncertainty management. 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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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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“…This is necessary to convert the physical object of the production environment into a digital model and thus provide the possibility to monitor it. The data thus transferred from the physical element to the connected DT can now be used to display basic information (e.g., ID, location, operating time), current status & metrics (e.g., for a machine, the current job and average processing time), and historical data in real-time (R1; R2; R3; R6) (Glatt et al, 2021;Krüger & Borsato, 2019;Kuehn, 2019;Papacharalampopoulos et al, 2021;Zhuang et al, 2018). In addition, the information prepared and displayed in this way must be displayed appropriately for the selected hierarchy level (R4) (Jiang et al, 2021;Kong et al, 2021).…”

Section: Objectives Of Solutionmentioning

confidence: 99%

Human-AI Interaction – Is It Trust or Emotions That Mediates Behavioral Intensions?

Yao,

Holopainen,

Laukkanen

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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The increasing requirements for industrial production environments due to customer expectations, the implementation of batch size 1, and further automation of production processes are confronting companies with new challenges. In particular, the emergence of cyber-physical systems is influencing and complicating manufacturing processes by capturing an increasing amount of information within production facilities. Digital twins are an interdisciplinary technology that may solve these issues because they serve to monitor, control, and optimize cyber-physical systems by creating a digital representation of realworld objects. Existing concepts for digital twins usually only consider individual machines without their context. This is of limited use for production environments due to a multitude of different machines and associated sensor types. Therefore, we propose a requirements catalog, concept, and prototypical implementation for the hierarchical structuring of digital twins in this paper.

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“…-Resilience, cybersecurity: The improvements in product security and resiliency concerning the availability of assets and processes are usually recurrent concerns in designing and building manufacturing processes that DTs can validate before the actual deployment and start-up of the system. Papacharalampopoulos et al [30] specify a roadmap for designing and implementing DTs to add agility and resilience to manufacturing. In particular, Empl et al [31] developed a cybersecurity framework based on DTs to analyze the vulnerabilities of IoT systems applying the SOAR (security orchestration, automation, and response) paradigm.…”

Section: Dt Concept and Its Adoptionmentioning

confidence: 99%

“…Ahmadi et al [55] discuss the role and evolution of humans as they interact with recent technologies and how future skills might fit with existing roles differently. The complexity of the DT model design and interpretation 8 [30,38,40,48,56,76,81,99] Bias, Coding bias 2 [14,60] Total analyzed articles 94 -Data quality: Incomplete documentation, binary data, accurate historical data on assets, cold starts, dark data, and siloed information cannot be collected appropriately.…”

Section: Total Analyzed Articles 94mentioning

confidence: 99%

Digital Twins in Software Engineering—A Systematic Literature Review and Vision

Guinea-Cabrera,

Holgado-Terriza

2024

Applied Sciences

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Digital twins are a powerful consequence of digital transformation. In fact, they have been applied to many industries to enhance operations, predict needs, improve decision making, or optimize performance, even though the definition of digital twins is still evolving. However, their impact on the software industry is still limited. Thus, this work aims to analyze the current adoption of digital twins in the software industry as a potential path to integrate them into application lifecycle management. To achieve this objective, first, the significant characteristics of current digital twins are analyzed in their application to manufacturing to understand how the knowledge and the lessons learned can be transferred to the software industry. Second, a systematic literature review was conducted on Scopus, the Web of Science, and the ScienceDirect database. The literature review revealed 93 documents after data screening and cleaning 251 initial documents. Our main findings are that digital twins are already influencing and will significantly affect the software industry, revolutionizing various aspects of the software development lifecycle. This study tackles what identifies a digital twin in the software industry, the specific domains and areas where they can be applied in the software lifecycle, and the proposed approaches explored to build digital twins for developing, deploying, and maintaining software systems. Finally, this study proposes some guidelines for building digital twins in the context of application lifecycle management. Determining an appropriate roadmap shortly is essential to achieve a widespread applicability to building suitable digital twins and preparing organizations for the software industry.

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Manufacturing resilience and agility through processes digital twin: design and testing applied in the LPBF case

Cited by 24 publications

References 25 publications

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

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

Human-AI Interaction – Is It Trust or Emotions That Mediates Behavioral Intensions?

Digital Twins in Software Engineering—A Systematic Literature Review and Vision

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