Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle

Huang, Yanqun; Zhang, Jie; Li, Xu; Tian, Liangjv

doi:10.1007/s00170-014-5606-0

Cited by 65 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the MOPM of MOO module, the PC scores and IEMSs, which are input and output variables, respectively, are continuous data. Wang et al [28] demonstrated that the prediction and fitting ability of neural network model for continuous data is more stable and stronger than the multiple linear regression model used in current authors' previous research [14], and the combination of GABP can further better predict the nonlinear mapping relationship between input and output variables [6,29,30].…”

Section: Genetic Algorithm and Back Propagation (Gabp)mentioning

confidence: 95%

See 1 more Smart Citation

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

et al. 2019

View full text Add to dashboard Cite

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method's high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method's good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form. Appl. Sci. 2019, 9, 2944 2 of 26 apply aggregation on the objectives to transform multiple objectives into a single objective rather than directly using MOEA in the MOO module [4]. Hsiao and Tsai [5] integrated the multiple objectives into a single value by using the linear weighting method, and then adopted a genetic algorithm (GA) to get the optimal product form based on the prediction model constructed by a fuzzy neural network (FNN). Guo et al. [6] also integrated multiple objectives into a single objective by using the linear weighting method, and obtained the optimal design by employing GA on the basis of the prediction model established by using a back propagation neural network (BPNN). This kind of study is simple and easy to implement, but because it limits the search space and excludes the consideration of all possible solutions, it has limited practical value for designers and consumers [7]. The second kind of study is to use MOEA directly in the MOO module. Although the application is more complicated, it has become a mainstream trend because it can better meet the performance requirements of PFDMOEAM. Yang [8] proposed a PFDMOEAM with a non-dominated sorting genetic algorithm II (NSGA-II) as MOEA in the MOO module, and used support vector regression (SVR) to provide multi-objective fitness functions required by NSGA-II. Su et al. [9] also constructed a PFDMOEAM with NSGA-II as MOEA. The difference is that fuzzy neural network (FNN) is used to provide fitness functions for NSGA II. Shieh et al.[10] also adopted NSGA-II as MOEA and combined with quantificat...

show abstract

Section: Genetic Algorithm and Back Propagation (Gabp)mentioning

confidence: 95%

“…Consequently, this paper uses GABP as the prediction technique in MOPM to achieve the high-precision prediction requirements of MOEA. For detailed implementation procedures of the technique, please see, for example, Huang et al [30].…”

Section: Genetic Algorithm and Back Propagation (Gabp)mentioning

confidence: 99%

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

et al. 2019

View full text Add to dashboard Cite

show abstract

“…There are many investigators have carried out error modeling research for complicated machinery system using MBS [41,23,44,39], mainly focus on designing and constructing a model to determine geometric error of machine tool and developing the key technique for compensation-identifying geometric error parameters. [43,17,4,7,22,2,11] introduce the methods of geometric error compensation, thermal error modeling, position error compensation, position-independent geometric errors modeling, volumetric error modeling and sensitivity analysis and establishing a product of exponential(POE) model for geometric error integration.…”

Section: Accuracy Predictionmentioning

confidence: 99%

A geometric accuracy design method of multi-axis NC machine tool for improving machining accuracy reliability

Cai¹,

Zhang²,

Cheng³

et al. 2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

View full text Add to dashboard Cite

show abstract

“…Compared with artificial intelligence models, such as NN (neural networks) [6,7] and GA (genetic algorithm) [8,9] etc, thermal error models based on different regression theories have advantages of easy calculation, strong practicality, and model parameters can be displayed [10 -12], which have been widely used in the field of thermal error compensation. Miao et al proposed a modeling method of principal component regression (PCR) algorithm [13], which can eliminate the influence of multi-collinearity among temperature variables.…”

Section: Introductionmentioning

confidence: 99%

Study on Temperature Measurement Point Optimization and Thermal Error Modeling of NC Machine Tools

Fan¹,

Guo²

2017

TOMEJ

View full text Add to dashboard Cite

Background:In precision machining, thermal error is the main source of machine tool error. And thermal error compensation is an effective method to reduce thermal error. Objective:In order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed. Method:This method is based on stepwise regression. According to the results of partial-F statistic, new variable is selected one by one, unapparent variables are deleted, and optimization selection of temperature measurement point is fulfilled, thermal error model of the NC machine tool is presented. Result:The new modeling method was used on NC machine tool, which reduced the temperature point number from 24 to 5. Moreover, model residual was less than 5µm after compensation. Conclusion:The result shows that the new thermal error model has higher prediction accuracy and less temperature variables.

show abstract

Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle

Cited by 65 publications

References 13 publications

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

A geometric accuracy design method of multi-axis NC machine tool for improving machining accuracy reliability

Study on Temperature Measurement Point Optimization and Thermal Error Modeling of NC Machine Tools

Contact Info

Product

Resources

About