A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing

Konstantinidis, Fotios K.; Myrillas, Nikolaos; Tsintotas, Konstantinos A.; Mouroutsos, Spyridon G.; Gasteratos, Antonios

doi:10.1080/00207543.2023.2270588

Cited by 19 publications

(1 citation statement)

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“…The traditional method of thermal compensation is to use coolant to cool the interior of the machine, but this method does not completely improve the thermal error of the machine tool [ 5 ]. Based on reviews by Konstantinidis et al [ 6 ], this research could be carried out at level 4 that covers advanced features such as intelligent object identification capabilities and metadata transformation techniques to address interoperability issues. In particular, classifiers with less data are typically trained via (semi)supervised machine learning.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Kristiani,

Wang,

Liu

et al. 2024

Sensors

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This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

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Section: Introductionmentioning

confidence: 99%