Thermal error prediction of ball screws based on PSO-LSTM

Gao, Xiangsheng; Guo, Yueyang; Hanson, Dzonu Ambrose; Liu, Zhihao; Wang, Min; Zan, Tao

doi:10.1007/s00170-021-07560-y

Cited by 33 publications

(12 citation statements)

References 36 publications

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“…The best acceptance rate R * RS in x •10 3 feasible proposal distributions is used to measure the performance of random search method, and parameter search time N s RS represents the total number of searches needed to find x • 10 3 feasible solutions which is used to illustrate the efficiency of random search method. 2) For HS, we consider three widely used heuristic algorithms for parameter search, which are the Genetic Algorithm (GA) [44], Particle Swarm Optimization (PSO) [45] and Differential Evolution (DE) [46]. On this basis, an Ensemble Model (EM) that aggregates the three heuristic methods is established to obtain the best proposal distributions.…”

Section: A Normal Mixture Density Casesmentioning

confidence: 99%

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

et al. 2023

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Random variate generation has been widely used in various engineering applications and model frameworks. The introduction of the proposal distribution makes this kind of methods can deal with more general sampling problems with complex probability density representation. However, the design of proposal distribution is usually difficult, especially when the target distribution is so complex that it contains asymmetric, multimodal and skewed, or even unsmooth, heavy tailed and illegal density, the sampling performance and efficiency will be greatly affected. In this paper, a novel parameter optimization method with efficient self-tuning strategy is proposed in order to automatically construct the optimal proposal distribution for any complex target density, which treats the design of proposal distribution as a parameter search process and search the optimal solution in an infeasible region by evaluating the loss of solution. The significant advantage of the proposed method is that the search based on infeasible region can converge to a good solution with only a few iterations, making our method far superior to other existing methods in efficiency, which is very suitable for the complex target distribution model with time-consuming density calculation process. Experimental results show the advantages of the proposed method in terms of search strategy, optimal solution performance, search efficiency and robustness.INDEX TERMS Monte Carlo methods, generative model, parameter optimization, rejection sampling, machine learning.

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Section: A Normal Mixture Density Casesmentioning

confidence: 99%

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

et al. 2023

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“…In [21], a LSTM-based ensemble learning was proposed for timedependent reliability analysis. Gao et al [22] used LSTM to construct a thermal error model for ball screws based on the thermal sensor signals. Zhou et al [23] proposed a prediction model for remaining useful life of tools based on LSTM using process parameter, workpiece information, and wear feature extracted from sensors.…”

Section: Related Workmentioning

confidence: 99%

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Xiong

2022

Preprint

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Determining reasonable tolerance patterns requires much knowledge and experience to balance the conflicting requirements. In previous studies, the process of tolerance specification has been based on the engineer’s experience and empirical data handed down from earlier projects. This paper proposes a learning model of tolerance specification of shaft part using deep learning architecture named CNN-BiLSTM-CRF, which is a combination of a Convolutional Neural Network, BiDirection Long Short-Term Memory network, and Conditional Random Field model. The CNN layer extracts the features from the CAD model; the Bi-LSTM layer composes the nonlinear relationship between tolerance specification and the features embedded by the CNN layer; the CRF layer predicts the tolerance sequence of the whole shaft part by jointly considering relations between neighbor tolerances. The proposed method directly takes the CAD model as the input and learns the tolerance specification from previous successful cases. Therefore, it does not require engineers to have any empirical knowledge.

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“…LSTM networks can capture both short-term correlation and long-term dependence. Gao et al 27 employed the thermal error model by using LSTM networks which was optimized by PSO, the PSO-LSTM model is established to precisely predict the thermal error of ball screws, and then provide a foundation for thermal error compensation. Qiu et al 28 performed a railway freight volume forecasting model by using LSTM networks optimized by PSO, and then the PSO-LSTM model has lower prediction error and higher prediction accuracy than the traditional LSTM and model GA-LSTM.…”

Section: Introductionmentioning

confidence: 99%

An improved particle swarm optimization using long short-term memory model for positioning control of a coplanar XXY stage

Huang

2022

Measurement and Control

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This paper presents XXY stage alignment with an image feedback system consisting of two charge-coupled devices (CCDs) and a proportional–integral–derivative (PID) image servo system tuned by particle swarm optimization (PSO). The initial stop values for the PSO algorithm often cause problems in calculation. A long short-term memory (LSTM) deep learning model can identify long-term dependences and sequential model data. Using LSTM to improve the PSO algorithm for searching best fitness value. LSTM predicts the fitness value of PSO, eliminating the need to preassess fitness, value, and uses the predicted fitness value to adjust the inertia weights of PSO adaptively. This allows the PSO search to be terminated in an early stage and reduces the time required for the search. Proposed method was applied to a visual servo system consisting of two CCD cameras and a personal computer–based PID controller for XXY stage motion. The experimental results indicate that LSTM can reduce the time required for PSO fitness search for controlling XXY stage motion under different conditions successfully. Through the training of the LSTM model, the stage positioning error and time of finding optimal control parameters for a coplanar XXY stage can reduced significantly for in-line inspection processes.

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Thermal error prediction of ball screws based on PSO-LSTM

Cited by 33 publications

References 36 publications

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

An improved particle swarm optimization using long short-term memory model for positioning control of a coplanar XXY stage

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