Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

Zhuang, Kejia; Sun, Yaobing; Gao, Zhongmei; Wang, Lei

doi:10.3390/sym13081438

Cited by 24 publications

(22 citation statements)

References 37 publications

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“…Because the workpiece rotates during turning, optical roughness measurement during machining is not possible, so the in-process tool wear measurement may be a potential solution for that. Several researchers have already dealt with this topic (e.g., [88][89][90][91]), but it would be worthwhile to examine the applicability of this idea from the perspective of the introduced method.…”

Section: Discussionmentioning

confidence: 99%

Theoretical Roughness Modeling of Hard Turned Surfaces Considering Tool Wear

Felhő

Varga

2022

Machines

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Surface roughness is an important factor in metal cutting, and usually different surface roughness characteristics are used to control the quality of the machined surfaces. However, as the cutting tool wears out during the cutting process, the roughness values change. In most cases, theoretical roughness values are calculated without taking the wear characteristics of the tool into account. For this reason, the calculated and measured roughness values may differ from each other, and the tendency of their change may also be different. This paper presents a method for the determination of the theoretical roughness of hard turned surfaces considering the wear of the cutting tool. The purpose of the analyses performed was to show the effect of wear trace on the tool and the roughness of the machined surface and to give a possible method to take the wear into account when calculating the theoretical roughness values. During the investigations, the shape of the actual (worn) edge section of the cutting tool was recorded by an optical microscope, and the theoretical surface roughness values were calculated with that profile by a CAD modeling method developed earlier. Cutting experiments were conducted on a lathe machine with two similar cutting tools, one of them has significant tool wear, while the other was a completely new one. The calculated theoretical roughness values were compared with real measured roughness values, and the error of the estimates was between 8.7 and 68.3%, larger errors were found at lower feeds.

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Section: Discussionmentioning

confidence: 99%

Theoretical Roughness Modeling of Hard Turned Surfaces Considering Tool Wear

Felhő

Varga

2022

Machines

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“…Physical-based Principle-based modelling [59]; finite element analysis [60]; computational fluid dynamics [68]; equivalent modelling [48] Data-driven Machine learning [53]; neural network [61]; deep learning [69] Hybrid Reduced-order modelling [30]; surrogate modelling [23] 8…”

Section: Methods Type Methodsmentioning

confidence: 99%

“…They used virtual sensors based on DT to construct performance prediction models for a feedwater heater. Virtual sensors driven by digital twin have also been applied in vertical transportation systems [47] and dredgers [53] to monitor guide alignment and defined residual warning values.…”

Section: Content Analysis Of Digital Twin Driven Conditionmentioning

confidence: 99%

Digital Twin-Driven Machine Condition Monitoring: A Literature Review

et al. 2022

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Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.

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“…The real-time data of grinding force and spindle motor current were acquired to estimate the optimal time for redressing the wheel, resulting in higher process efficiency. More example cases of DT in manufacturing can be seen in the works of Zhuang et al [93], Botkina et.al [15], Zhang et.al [85], and Armendia et.al [8].…”

Section: Digital Twin In Manufacturingmentioning

confidence: 99%

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

Christiand¹,

Kiswanto

Baskoro³

2023

Preprint

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This paper presents a novel digital twin of the micro-milling process that can indirectly monitor the micro-tool wear progression by making inferences from the real-time and simulated variables of the micro-milling process. With its wear monitoring service, the digital twin is regarded as the new approach in the field of tool wear monitoring (TWM) systems. The dynamics of the micro-milling process are simulated by using physics-based models, such as spindle motor, spindle controller, and cutting torque models with real-time data from the actual micro-milling machine. The advantage of the proposed digital twin is that the wear monitoring can adaptively adjust to the main machining parameters, such as feed rate and spindle speed. Therefore, exhaustive training of the models is not needed whenever the machining parameters change. The performance of the digital twin in monitoring the wear progression has been evaluated through several slot micro-milling experiments of the stainless steel workpiece. The evaluation and analysis of the experiment result concluded that the proposed digital twin could detect wear progression abnormality by using the estimate discrepancy. Furthermore, the wear severity could be recognized using the final wear estimation value.

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Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

Cited by 24 publications

References 37 publications

Theoretical Roughness Modeling of Hard Turned Surfaces Considering Tool Wear

Theoretical Roughness Modeling of Hard Turned Surfaces Considering Tool Wear

Digital Twin-Driven Machine Condition Monitoring: A Literature Review

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

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