Wyniki wyszukiwania - BazTech

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Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718

Zhou Yuqing, Sun Wei, Ye Canyang, Peng Bihui, Fang Xu, Lin Canyu, Wang Gonghai, Kumar Anil, Sun Weifang

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 2

art. no. 165926

EN

Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.

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Finite element analysis on pushing the molar backwards using invisible aligner with different migration displacement

Zhang Jiwu, Ma Lili, Ren Yuzhongxiu, Zhou Yuqing, Wei Rong, Rong Qiguo

Acta of Bioengineering and Biomechanics

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2023

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Vol. 25, no. 2

115--123

EN

This paper examines the biomechanical mechanism behind the effect of the invisible aligner technique on tooth movement processes. Methods: To compare the effects of different target positions on tooth movement and the periodontal ligament (PDL), two kinds of aligners were designed to provide displacements of 0.2 mm (Model A) and 0.3 mm (Model B). Different displacements of the maxillary second molar were simulated using the finite element (FE) method. Results: The results of numerical simulations showed that the maximum stress was in the PDL of the distal surface and the palatal surface. The stress of the PDL in Model B was larger than Model A, with the displacement of the second molar 0.027 mm in Model A, by 44.9% lesser than that in Model B. Conclusions: The aligner that provided a displacement of 0.2 mm was more suitable for pushing the second molar backward in the initial stage. During the tooth movement processes, the displacement of the crown was larger than that of the root and the displacement decreased gradually from the crown to the root. In addition, the displacement and rotation of teeth during orthodontic treatment were measured and analysed.

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Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

Zheng Guoxiao, Sun Weifang, Zhang Hao, Zhou Yuqing, Gao Chen

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

612--618

EN

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

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A tool wear condition monitoring approach for end milling based on numerical simulation

Zhu Qinsong, Sun Weifang, Zhou Yuqing, Gao Chen

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 2

371--380

EN

As an important research area of modern manufacturing, tool condition monitoring (TCM) has attracted much attention, especially artificial intelligence (AI)- based TCM method. However, the training samples obtained in practical experiments have the problem of sample missing and sample insufficiency. A numerical simulation- based TCM method is proposed to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology, in which the KL divergence and cosine similarity are used as the evaluation indexes. Second, samples under various tool wear categories are obtained by the optimized numerical model above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling TCM experiments. The results indicate the classification accuracies of four classifiers (SVM, RF, DT, and GRNN) can be improved significantly by the proposed TCM method.