A review of digital twin-driven machining: From digitization to intellectualization

Liu, Shimin; Bao, Jinsong; Zheng, Pai

doi:10.1016/j.jmsy.2023.02.010

Cited by 50 publications

(8 citation statements)

References 97 publications

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“…Digital twin, is de ned as an integrated multi-physics, multi-scale, probabilistic simulation of an as-built product, system, or process which can mirror the life of its corresponding twin using available physical models, history knowledge, and real-time data [73], is nowadays regarded as the key for the convergence of physical systems and cyber systems [74]. The vigorous development of digital twin technology has provided a novel solution for both intelligent machining and Zero Defect Manufacturing (ZDM), demonstrating its potential in product quality management [75,76]. Building upon this foundation and drawing from the accomplishments of our research group in digital twin [74,77,78], this paper proposes the integration of GIAM with digital twin technology to evaluate and verify the process knowledge and plans generated by ProcessGPT, thereby enhancing the reliability and explainability of GIPP.…”

Section: Digital Twin-based Veri Cation Methodsmentioning

confidence: 99%

Generative AI and digital twin integrated intelligent process planning：A conceptual framework

Xu,

Zhou,

Zhang

et al. 2023

Preprint

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Process planning serves as a critical link between design and manufacturing, exerting a pivotal influence on the quality and efficiency of production. However, current intelligent process planning systems, like computer-aided process planning (CAPP), still contend with the challenge of realizing comprehensive automation in process decision-making. These obstacles chiefly involve, though are not confined to, issues like limited intelligence, poor flexibility, low reliability, and high usage thresholds. Generative artificial intelligence (AI) has attained noteworthy accomplishments in natural language processing (NLP), offering new perspectives to address these challenges. This paper summarizes the limitations of current intelligent process planning methods and explores the potential of integrating generative AI into process planning. With synergistically incorporating digital twins, this paper introduces a conceptual framework termed generative AI and digital twin-enabling intelligent process planning (GIPP). The paper elaborates on two supporting methodologies: process generative pre-trained transformer (ProcessGPT) modelling and digital twin-based process verification method. Moreover, a prototype system is established to introduce the implementation and machining execution mechanism of GIPP for milling a specific thin-walled component. Three potential application scenarios and a comparative analysis are employed to elucidate the practicality of GIPP, providing new insights for intelligent process planning.

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Section: Digital Twin-based Veri Cation Methodsmentioning

confidence: 99%

Generative AI and digital twin integrated intelligent process planning：A conceptual framework

Xu,

Zhou,

Zhang

et al. 2023

Preprint

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“…In 2023, Liu et al [26] made a state-of-the-art survey of the existing literature on welding image recognition using deep learning. It was first stated that the number of articles involving this topic grew exponentially in the last years, reinforcing the argument that AI is a growing subject whose techniques are being more and more applied and developed.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Techniques to a Sensorial Module

Pimentel,

Gonçalves,

Pinto

et al. 2024

2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE)

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Nowadays, society is in a climate of uncertainty regarding energy and resource sustainability, which is why efforts to combat this problem are made and encouraged daily. This is no different in the industry, where there is a continuous work to improve manufacturing processes in order to achieve utopian zero defect manufacturing. Alongside this, the emergence of new technologies, the development of the computing power of machines and the digitisation of processes in the context of Industry 4.0 have been fundamental to the production of data that feeds and develops artificial intelligence models.This dissertation will address a quality control problem in a welding process in the automotive industry. It is believed that the development of a predictive quality control system using artificial intelligence techniques can solve the problem in question.In order to study the problem, and due to the impossibility of working with the real problem, this work includes a study of the techniques expected to be applied in the final solution. As well as being theoretical, this study of methods has a practical component, applying them to data collected from a testbed. This testbed is a physical prototype with a parameterised process, also developed as part of this dissertation. In addition, a dataset referring to a laser welding process was also analysed to complement the study of the algorithms with data similar to data from the original problem.In this document, the scope of the work is framed within the current state of the art, followed by a theoretical reflection on the subjects covered. It is decided to study and apply the following supervised learning techniques to the data collected: K-Nearest Neighbours, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression, Random Forest and Gradient Boosting. This is followed by a description of the test base developed and the methodology for collecting and processing the data.It is concluded that although the models built with the Support Vector Machine algorithms showed satisfactory results for modelling the laser welding process, the data collected from the test base was unreliable, and different models can perform differently in different situations.

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“…A digital twin represents the physical object, process, or system in virtual representation [32]. It includes both the physical and digital domains through duplicated real-time data from the physical system with a simulation model to enhance its physical counterpart's behavior, characteristics, and performance [33]. It works as a link between the real and simulated model to allow real-time monitoring, analysis, and optimization of the system [34].…”

Section: Digital Twin Fundamentals and Application In Ambmentioning

confidence: 99%

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Hu,

Taha,

Yang

2024

Applied Sciences

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Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.

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A review of digital twin-driven machining: From digitization to intellectualization

Cited by 50 publications

References 97 publications

Generative AI and digital twin integrated intelligent process planning：A conceptual framework

Generative AI and digital twin integrated intelligent process planning：A conceptual framework

Application of Artificial Intelligence Techniques to a Sensorial Module

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

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