Article
Materials Science, Paper & Wood
Aleksandar Rakic, Sasa Zivanovic, Zoran Dimic, Mladen Knezevic
Summary: This paper presents an application of an open architecture control system on a multi-axis wood CNC milling center as a digital twin control, aiming to validate the effectiveness of the developed control system and its performance.
Article
Automation & Control Systems
Ruijuan Xue, Xiang Zhou, Zuguang Huang, Fengli Zhang, Fei Tao, Jinjiang Wang
Summary: This paper proposes a comprehensive framework for assessing the performance of a spindle driven by digital twin, using multi-domain modeling and index system construction. The method combines subjective and objective weights to comprehensively assess the spindle performance level, and has been verified to be effective and feasible for on-site application.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Industrial
Zexuan Zhu, Xiaolin Xi, Xun Xu, Yonglin Cai
Summary: This paper introduces a Digital Twin-driven thin-walled part manufacturing framework, which utilizes Digital Twin technology to improve the efficiency of thin-walled parts machining and manage trial machining processes in real-time through interactive digital data.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Automation & Control Systems
Rob Ward, Chao Sun, Javier Dominguez-Caballero, Seun Ojo, Sabino Ayvar-Soberanis, David Curtis, Erdem Ozturk
Summary: The future of machining lies in fully autonomous machine tools. Development of new technologies for predicting, sensing and making intelligent decisions autonomously is crucial. This research presents a machining Digital Twin capable of real-time adaptive control of intelligent operations, addressing the slow implementation of Digital Twins in machining due to computational burdens. The proposed system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts, with validation case studies conducted for each application.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Automation & Control Systems
Ruijuan Xue, Peisen Zhang, Zuguang Huang, Jinjiang Wang
Summary: This paper proposes a digital twin-driven fault diagnosis method for CNC machine tools, which establishes and validates a digital twin model and uses model data fusion method and decision tree algorithm to achieve fault diagnosis. Experimental results show that the proposed method can effectively diagnose the stiffness deterioration fault of CNC machine tools.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
V. S. Vishnu, Kiran George Varghese, B. Gurumoorthy
Summary: This paper presents a data-driven digital twin framework that predicts KPIs in CNC machining. The predicted KPIs can assist decision-makers in choosing cutting parameters to achieve the required KPIs. The paper focuses on two KPIs, energy and surface roughness, and their application in the proposed digital twin using experimental data. The work also discusses the choice of predictive modelling methods and their outcomes in CNC machining.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Engineering, Electrical & Electronic
Mohammed Masum Siraj Khan, Jairo Giraldo, Masood Parvania
Summary: This article introduces a novel real-time attack localization strategy for power distribution systems using a Digital Twin (DT) as a cyber-physical reference model. The proposed method computes a new metric, Residual Rate of Change (RRC), to differentiate False Data Injection (FDI) attacks and determine their locations. Experimental results demonstrate the effectiveness of the proposed method in localizing different types of attacks.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Automation & Control Systems
Mingyi Guo, Xifeng Fang, Zhongtai Hu, Qun Li
Summary: This paper focuses on common problems in the manufacturing of numerical control machine tools and proposes a new system architecture using digital twin technology to solve these problems. The improved algorithm allows for more accurate detection of collision information between tools and machine tools, and the display of more realistic workpiece shapes. Furthermore, online tool wear monitoring can be achieved through model synchronous motion driven by production perception data.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Automation & Control Systems
Jianxin Guo, Mingyong Zhao, Lixian Zhang
Summary: This paper proposes a time-bound optimal planning model to balance the cutting efficiency and the cutting security. By considering the kinematic constraints as a fuzzy set and using fuzzy optimization method, a compromise bound is obtained. The original problem is simplified into a convex problem and solved numerically.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Tim von Hahn, Chris K. Mechefske
Summary: This article demonstrates several best practices and challenges discovered while building an ML system to detect tool wear in metal CNC machining. By optimizing data infrastructure, starting with simple models, being aware of data leakage, using open-source software, and leveraging advances in computational power, a deployable ML system is achieved in a real-world manufacturing environment.
Article
Computer Science, Interdisciplinary Applications
Dimitrios Pantazis, Paul Goodall, Sarogini Grace Pease, Paul Conway, Andrew West
Summary: This research develops a Virtual Machining Energy Toolkit (V_MET) to predict the electrical power consumption of a CNC milling machine. It enables the evaluation of the energy impact of manufacturing part programs prior to implementation and real-time monitoring of the process.
COMPUTERS IN INDUSTRY
(2023)
Article
Chemistry, Analytical
Jung-Sing Jwo, Cheng-Hsiung Lee, Ching-Sheng Lin
Summary: Introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable due to the complexity of the aerospace and defense industry. This study proposes the concept of Data Twin to simplify high-fidelity virtual models and uses machine learning approaches to achieve it. A microservice software architecture, Cyber-Physical Factory (CPF), is also proposed to simulate the shop floor environment, along with two war rooms for establishing a collaborative platform.
Article
Multidisciplinary Sciences
S. Ganesh Kumar, Bipin Kumar Singh, R. Suresh Kumar, Anandakumar Haldorai
Summary: A Digital Twin (DT) is a virtual representation of a product system that analyzes its functions and properties. DT has significant impacts in various fields by increasing productivity and reducing wastage. This article focuses on developing a DT model of a Lathe machine for Tool Condition Monitoring (TCM). Implementing DT in industries is challenging, especially when simulating online cutting forces and wear. While research on tool condition prediction using machine learning and Artificial Neural network models has been done, there is limited research on digital twins for TCM. This article provides a technique for implementing the DT model of a lathe tool and verifies its feasibility through a case study. The DT model is able to monitor and predict tool conditions, contributing to increased productivity and predictive maintenance in machining.
DEFENCE SCIENCE JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Jinfeng Liu, Xiaojian Wen, Honggen Zhou, Sushan Sheng, Peng Zhao, Xiaojun Liu, Chao Kang, Yu Chen
Summary: This paper presents a multidimensional modeling approach for machining processes using Digital Twin technology, which supports the design and execution phases of intelligent machining. The effectiveness of the applied framework and the proposed method is verified through testing key components of diesel engines.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Engineering, Manufacturing
Markus Brillinger, Marcel Wuwer, Muaaz Abdul Hadi, Franz Haas
Summary: This paper discusses the trend of policies shifting CO2 emissions from fossil fuels to renewable energy sources, and governments' efforts to reduce energy consumption. Not only large industries, but also small and medium enterprises, as well as enterprises with production lots of one, are now required to reduce their energy demands in production. Through machine learning algorithms, particularly the 'RandomForest' algorithm, accurate predictions of energy demand in CNC machining operations can be achieved.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Automation & Control Systems
Kuo Liu, Te Li, Haibo Liu, Yu Liu, Yongqing Wang
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Automation & Control Systems
Yongqing Wang, Jiaxin Liu, Kuo Liu, Zhaohuan Liu, Siqi Wang, Minghua Dai
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2020)
Article
Automation & Control Systems
Yongquan Gan, Yongqing Wang, Kuo Liu, Lingsheng Han, Qi Luo, Haibo Liu
Summary: Traditional PTFE manufacturing involves molding and machining methods. Molding is suitable for mass production, while machining is more efficient for small-scale or special-shaped products.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Automation & Control Systems
Kuo Liu, Lei Song, Haibo Liu, Wei Han, Mingjia Sun, Yongqing Wang
Summary: An improved response surface method based on quadratic polynomial approximation was proposed to calculate the reliability of the model for spindle thermal growth error. It was found that the model shows strong robustness when the thermophysical parameters change.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Automation & Control Systems
Yongqing Wang, Bo Qin, Kuo Liu, Mingrui Shen, Mengmeng Niu, Lingsheng Han
Summary: The article introduces a multitask learning method based on deep belief networks for predicting tool wear and part surface quality. Experimental results show that the proposed method can improve prediction accuracy and reduce computing time.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yongqing Wang, Mengmeng Niu, Kuo Liu, Mingrui Shen, Bo Qin, Honghui Wang
Summary: The article proposes a new data augmentation method based on CoralGAN for predicting part surface roughness, addressing the issues of high collection cost, unbalanced categories, and complicated data distribution. The proposed method improves the prediction accuracy of part surface roughness, as demonstrated in experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yongquan Gan, Yongqing Wang, Kuo Liu, Yuebing Yang, Shaowei Jiang, Yu Zhang
Summary: This study reveals the machining mechanism of TA15 under cryogenic cooling conditions by analyzing the material properties under different low temperatures. The results show that machining TA15 under cryogenic conditions can reduce plasticity and adhesion, improve the machining surface quality, and effectively reduce tool adhesion wear.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Lei Song, Kuo Liu, Di Zhao, Song Zhang, Zewei Zhang, Yongqing Wang
Summary: A spindle axial time-varying thermal error compensation method based on edge computing was studied to reduce the impact of the error on the accuracy consistency of high-end parts in horizontal boring and milling machine tool (HBMMT). The error model was established based on the speed and acceleration of spindle temperature change, and the compensation value was acquired using edge computing. The error was then compensated by offsetting the coordinate origin, resulting in a reduced error fluctuation range and improved accuracy stability of HBMMT.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)