Development of a Grey online modeling surface roughness monitoring system in end milling operations

Huang, PoTsang B.; Zhang, Huang-Jie; Lin, Yi‐Ching

doi:10.1007/s10845-017-1361-z

Cited by 31 publications

(15 citation statements)

References 40 publications

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“…?6.0 platform was established to meet effective production [213]. A grey online modeling surface roughness monitoring system was developed to accurately predict the surface roughness in end milling [214]. Machine center fault diagnosis and prediction based on data mining were studied to develop a systematic approach and obtain predictive maintenance knowledge in the Industry 4.0 era [215].…”

Section: 321mentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Section: 321mentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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“…Sun et al predicted the nitrous oxide emissions for 2030 in six countries based on three advanced mathematical grey prediction models [14]. Huang et al proposed a grey online modeling surface roughness monitoring system to predict the surface roughness in end milling operations [16]. Li et al constructed a grey model for concrete acidification prediction; they analyzed the effects of the pH value, concrete cover thickness, and surface coating on the service life [17].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of High‐Frequency Swing Self‐Lubricating Liner

Hao

Wang

Chen

et al. 2021

Shock and Vibration

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The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.

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“…After defining factors including machine tools, workpieces, and machining methods, the main factors influencing cutting forces and vibration are cutting factors, tool wear, tool wear cutting force, and cutting temperature during the metal cutting process [24][25][26]. As tool wear increases, the cutting force and power increase too [27,28]. A study reported a mathematical model of the force of face milling, taking into account machining parameters and tool wear [29,55].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction

Chen¹,

Gao²

2021

Preprint

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Often, engineers with machining experience often judge machining state and tool life according to chips’ features. Engineers' experience is digitized in this study. During the cutting process, the cutting tool coming in contact with the workpiece produces a shear zone, which causes plastic deformation and shear slip. The chips closest to the shear zone can directly show the state of the tool and workpiece when the material is SKD61. This study used chip color, vibration, and current signal integration for prediction of machining state and cutting tool life. When the cutting tool wears increased, the chip surface color changed in the following way: purpleè purple blueè blue ècyan, or even green and yellow. When the cutting tool was in the accelerating wear phase, the color change was particularly obvious. The Back-Propagation Levenberg–Marquardt (BP-LM) predictive methodology was used to compare the predictive ability of voltage, vibration signal, and chip color. The Mean Absolute Percentage Error (MAPE) for the voltage signal was 12.28%, for the vibration signal it was 11.38%, and for the chip color combined with multi-sensor characteristics it was 7.85%. The MAPE of the chip color was the smallest. Using the General Regression Neural Network (GRNN) methodology, the MAPE for the voltage signal was 10.74%, for the vibration signal 7.96%, and for the chip color combined with multi-sensor characteristics was 6.59%. The MAPE of the chip color was the smallest. Obviously, the chip color combined with multi-sensor signals provided better predictive results than the vibration signal or voltage signal alone. There is currently no research on the usefulness of monitoring chip color for tool life prediction.

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Development of a Grey online modeling surface roughness monitoring system in end milling operations

Cited by 31 publications

References 40 publications

Digital twin-based sustainable intelligent manufacturing: a review

Digital twin-based sustainable intelligent manufacturing: a review

Remaining Useful Life Prediction of High‐Frequency Swing Self‐Lubricating Liner

A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction

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