Prediction of the dynamic performance for the deployable mechanism in assembly based on optimized neural network

Yu, Dewen; Guo, Junkang; Zhao, Qiangqiang; Hong, Jun

doi:10.1016/j.procir.2020.05.249

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…Another method is to use extended convolution. Compared with general convolution, in addition to the size of the convolution kernel, the dilation rate used to represent the size of the extended convolution is increased [16].…”

Section: Extended Convolutionmentioning

confidence: 99%

“…Considering that the traffic flow has a weekly periodicity, the traffic flow at the current moment is similar to that at the same time last week, and the difference is used to subtract the traffic flow at the same time last week from the traffic flow at the current moment. (2) LSTM1 and LSTM2 represent single-layer LSTM and double-layer LSTM, respectively, and the number of hidden layer units is 64; (3) GRU1 and GRU2 indicate that single-layer GRU and double-layer GRU are used, respectively, and the number of hidden layer units is 64; (4) e expansion coefficient of each layer of DCFCN is [1,2,4,8,16,32], the number of convolutional kernels of each layer is 32, and the size of convolutional kernels is 4. As can be seen from Table 2, compared with other comparison models, the proposed DCFCN has the best prediction effect and the lowest in RMSE, MAE, and MAPE indicators.…”

Section: Experimental Settingsmentioning

confidence: 99%

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Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Wang

2022

Mobile Information Systems

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In order to explore a mobile virtual reality railway traffic congestion prediction algorithm based on convolutional neural network, an expanded causal convolution neural network (DCFCN) was proposed, which introduced the expanded convolution to increase the size of the receptive field and obtain the long-term memory of the sequence. At the same time, causal convolution is introduced to solve the problem of information leakage. DCFCN is made up of 6 convolutional layers, each layer achieves causal convolution through padding, and the expansion coefficient increases exponentially layer by layer. Experimental results show that LSTM and GRU can obtain the time sequence relationship of mobile virtual reality traffic flow sequence, and the prediction effect is better than simple method and traditional ARIMA model, but still inferior to DCFCN. The RMSE of DCFCN decreased by 0.38 compared with single-layer LSTM, 0.52 compared with double-layer LSTM, and 0.38 compared with single-layer and double-layer GRU. It shows that TCN model can indeed do better than RNN in sequence modeling. It is proved that the proposed DCFCN is superior to other comparison models in mobile virtual reality traffic flow prediction, and the computational efficiency on GPU is significantly improved.

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Section: Extended Convolutionmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Wang

2022

Mobile Information Systems

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“…Examples encompass numerical algorithms analyzing the final unfolding angles of adjusted joints in a linkage, neural networks predicting final errors for achieving higher accuracy, and adaptive support vector machines (ASVMs) based on the SVM framework predicting the assembly quality of automotive sunroofs. Alternative approaches include using artificial intelligence techniques to narrow down the search space for assembly sequence planning, considering an analysis of the limitations of existing methods [20][21][22]. Leveraging the potent fitting capability of machine learning, these methods can yield superior results compared to traditional algorithms.…”

Section: Introductionmentioning

confidence: 99%

Predicting Assembly Geometric Errors Based on Transformer Neural Networks

Wang,

Li,

Liu

et al. 2024

Machines

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Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models.

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“…Ding et al (2020) studied the deployable mechanism of cable winding device and proposed a novel solution to improve the accuracy of the assembly. Yu et al (2021) present an efficient assembly optimizer of the deployable mechanism based on machine learning and predict the dynamic performance through the neural network to improve the assembly accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on assembly accuracy of deployable arm based on Jacobian-Torsor model

et al. 2023

RIA

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Purpose The purpose of this study is to establish an assembly accuracy analysis model of deployable arms based on Jacobian–Torsor theory to improve the assembly accuracy. Spacecraft deployable arm is one of the core components of spacecraft. Reducing the errors in assembly process is the main method to improve the assembly accuracy of spacecraft deployable arms. Design/methodology/approach First, the influence of composite connecting rod, root joint and arm joint on assembly accuracy in the tandem assembly process is analyzed to propose the assembly accuracy analysis model. Second, a non-tandem assembly process of “two joints fixed-composite rod installed-flange gasket compensated” is proposed and analyzed to improve the assembly accuracy of deployable arms. Finally, the feasibility of non-tandem assembly process strategy is verified by assembly experiment. Findings The experiential results show that the assembly errors are reduced compared with the tandem assembly process. The errors on axes x, y and z directions decreased from 14.1009 mm, 14.2424 mm and 0.8414 mm to 0.922 mm, 0.671 mm and 0.2393 mm, respectively. The errors round axes x and y directions also decreased from 0.0050° and 0.0053° to 0.00292° and 0.00251°, respectively. Originality/value This paper presents an assembly accuracy analysis model of deployable arms and applies the model to calculate assembly errors in tandem assembly process. In addition, a non-tandem assembly process is proposed based on the model. The experimental results show that the non-tandem assembly process can improve the assembly accuracy of deployable arms.

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Prediction of the dynamic performance for the deployable mechanism in assembly based on optimized neural network

Cited by 8 publications

References 1 publication

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Predicting Assembly Geometric Errors Based on Transformer Neural Networks

Research on assembly accuracy of deployable arm based on Jacobian-Torsor model

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