Time-Varying Error Prediction and Compensation for Movement Axis of CNC Machine Tool Based on Digital Twin

Liu, Kuo; Song, Lei; Han, Wei; Cui, Yiming; Wang, Yongqing

doi:10.1109/tii.2021.3073649

Cited by 57 publications

(15 citation statements)

References 22 publications

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“…The work of Liu et al focuses on the development of DT for the study of temporal error calculation and compensation for the movement of linear axes in CNC Machine tools. These models helped in the reduction of hole pitch error range by 69.19% [10].…”

Section: Related Workmentioning

confidence: 99%

Enabling Smart IIoT based manufacturing using Self-Adaptive Digital Twin of CNC Machines

mishra¹

2022

Preprint

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<p>Computerized Numerical Control (CNC) machines are the building block of industrial manufacturing, while CNC machines (CNCM) are designed to work relentlessly, breakdown may occur due to ageing, wrong operation, malfunctioning of sensors/actuators etc. Breakdown of CNCM hampers the production, supply chain and occurs financial loses. To improve the availability of the CNCM, predictive maintenance is considered a gold standard which requires continuous monitoring of the state of CNCM. The state of the CNC machine is temporal and requires a continuous data stream (CDT) to represent the correct present state. To achieve CDT, we propose and evaluate the Self-Adaptive Digital Twin (SADT) model of the CNC Machine (CNCM) using open-source industry-standard OPCUA protocol in compliance with RAMI4.0 architecture. We tested our approach on actual CNC machines in an industrial setup in production environment. We found that the proposed SADT with data analytics can identify potential issues in the CNCM, various bottlenecks in production and provides real-time notifications for fault prediction, which leads to the optimization of spares and improved predictive maintenance. We presented a few applications of the proposed SADT and found that it helps in optimising the axis closed loop control, thereby reducing the vibrations and the following error by five times. It also predicts the runtime of the part program accurately in real-time by analysing the current feed rate. We observed a 15% increase in Mean Time Between Failure (MTBF), 11% decrease in Mean Time to Repair (MTTR), 21% reduction in the percentage of breakdowns and 14.2% reduction in the power consumption of the CNCM after the implementation of the proposed system.</p>

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Section: Related Workmentioning

confidence: 99%

Enabling Smart IIoT based manufacturing using Self-Adaptive Digital Twin of CNC Machines

mishra¹

2022

Preprint

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“…Depending on its use-case, DT can be used to mirror systems across the whole spectrum of complexity, from unitary elements, such as the movement axis of the Computerized Numerical Control (CNC) machine tool [87], to an entire fleet of aircraft [88]. The virtualisation of composite heterogeneous machines or services always requires heavy computational prowess.…”

Section: B Cloud Fog and Edge Computingmentioning

confidence: 99%

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Mihai

Yaqoob

Hưng

et al. 2022

IEEE Commun. Surv. Tutorials

286

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“…Actually, DT has achieved initial success in autonomously controlling of a single manufacturing device. For example, Zhao et al [16] established a DT-driven cyber physical system for the context-aware autonomously controlling of a micro-dots punching machine tool; Kaigom and Roßmann [17] introduced a concept for the development of robotic digital twins that help companies gain a competitive edge through an intelligent robotized automation; Liu et al [18] introduced a DT-based framework for time-varying error prediction and compensation for the movement axis of a CNC machine tool. Comparatively speaking, DT-driven systemlevel control is still in its infancy, where most current related researches [19], [20], [21] focus on providing control suggestions based on simulation or optimization results to guide the manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

et al. 2022

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The booming personalized and customized demands of customers in Industry 4.0 pose great challenges for manufacturing enterprises in terms of flexibility and responsiveness. Nowadays, many effective dynamic scheduling approaches have been proposed for manufacturing systems to quickly respond to changes in customer demands, where, however, the implementation of an automatic programming method with high control accuracy and low control delay is still challenging. The above unaddressed issue brings about a lot of labor-intensive and time-consuming manual offline programming work when adjusting the scheduling scheme to meet dynamic customer demands, resulting in limited flexibility and responsiveness in current manufacturing systems. To bridge this gap, a bi-level adaptive control architecture enabled by an automatic programming method is proposed and embedded into a digital twin manufacturing cell (DTMC). The bi-level architecture aims to automatically map an input task scheduling scheme with a batch of jobs into a group of control programs through a behavior model network and a set of event models embedded in DTMC. It also provides an adaptive program modification mechanism to quickly adapt to the dynamic adjustment of the scheduling scheme caused by the changing of customer demands or production conditions, thus equipping DTMC with strong flexibility and responsiveness. Based on the bi-level architecture, a DTMC prototype system is developed, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach.

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Time-Varying Error Prediction and Compensation for Movement Axis of CNC Machine Tool Based on Digital Twin

Cited by 57 publications

References 22 publications

Enabling Smart IIoT based manufacturing using Self-Adaptive Digital Twin of CNC Machines

Enabling Smart IIoT based manufacturing using Self-Adaptive Digital Twin of CNC Machines

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

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