Process optimization of high-speed dry milling UD-CF/PEEK laminates using GA-BP neural network

Cao, Huajun; Liu, Lei; Wu, Bo; Gao, Yuan; Qu, Da

doi:10.1016/j.compositesb.2021.109034

Cited by 51 publications

(16 citation statements)

References 44 publications

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“…Firstly, there is no standard selection rule for the number of neurons in the hidden layer, and then, in the process of training, it is easy to produce a local optimal solution, which affects the predictive accuracy. [ 39 ] In order to improve the predictive accuracy of BP neural network, the strong global optimization ability and high convergence efficiency of GA is used to optimize the structure parameters of neural network, and establish GA‐BP neural network, which can effectively improve the shortcomings of traditional BP neural network and improve the predictive accuracy.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of curing process for thermosetting prepreg compression molding process based on machine learning

Hou

You

et al. 2022

Polymer Composites

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In the curing process of thermosetting prepreg compression molding (PCM), the distribution of the temperature field and the curing degree field have an important influence on the performance of composites. Therefore, the establishment of method to accurately predict the temperature difference and the degree of cure (DoC) difference during the curing process is significance for improving the performance of composites. In this paper, three kinds of machine learning models are studied: back propagation (BP) neural network, genetic algorithm‐back propagation (GA‐BP) neural network, radial basis function (RBF) neural network, then predictive models based on finite element method (FEM) and machine learning models are proposed. In the double‐dwell curing curve, six typical parameters are selected as inputs; the maximum value of temperature, the maximum value of temperature overshoot, the maximum DoC difference, the curing time, these four parameters during the curing process are selected as outputs, then the rapid predictive model is established. Within the value range of the process parameters, the Latin hypercube sampling (LHS) method is used to select 100 sets of sample points, and after training on three predictive models, comparison, and verification are carried out. The results show that the predictive effect of the RBF model is the best. In these three models, the RBF model is more suitable for the performance prediction of composites PCM. In this article, the research provides the basis for the performance prediction of composites and the multiobjective optimization of the curing process.

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Section: Methodsmentioning

confidence: 99%

“…The predictive accuracy is affected by the size and effectiveness of training sets. According to the analysis of literature, [ 21,28,39 ] in this article, 100 sets of data are used for training. The steps are as follows. Determine the input layer nodes.…”

Section: Methodsmentioning

confidence: 99%

Prediction of curing process for thermosetting prepreg compression molding process based on machine learning

Hou

You

et al. 2022

Polymer Composites

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“…During the choice of ECTs for all assessment objects, the following values are considered: T cut = const-the depths of cut, mm; D = constdiameter of the workpiece, mm; l = const-the estimated length of the cutting stroke in the direction of the feed movement, mm. The mathematical model of situational management system [24] for the cutting process takes the following form:…”

Section: A General Model For Choosing An Edge Cutting Tool For Optima...mentioning

confidence: 99%

“…In [24], a system for surface defects classification made under milling CRF/PEEK was suggested for the prediction model of surface quality considering fiber orientation, cutting speed, feed per tooth, and cutting width using a neural network optimized for the genetic algorithm (GA-BP). The results' prediction shows that the model has sufficient generalization capability with a prediction accuracy above 90.39%.…”

Section: Introductionmentioning

confidence: 99%

Method for an Effective Selection of Tools and Cutting Conditions during Precise Turning of Non-Alloy Quality Steel C45

et al. 2022

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The paper presents a constructing methodology for a modern approach to tools selection and solving the problem of assigning optimal cutting parameters for specific production conditions. The mathematical formulation determining the extreme values of the technological process optimality criteria is obtained. A system of technical and economic quality indicators for cutting tools is proposed. This system allows principles’ implementation of decentralization and interoperability “Industry 4.0” via finite element modeling of the cutting process based on solving the problem of orthogonal free cutting modeling. The proposed methodology further usage is possible by creating a standardized database on the parameters of the tool: the adhesive component of the friction cutting coefficient for processing of a specific pair of cutting and tool materials (or tool coating material) and the impacts of the cutting-edge radius on cutting efficiency of a particular material.

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“…However, the traditional BP neural network has the defects that it is easy to fall into local extremum and the convergence speed is slow or even non convergent [53]. In view of this defect, scholars use genetic algorithm (GA) to optimize BP neural network [54,55] and improve the convergence speed. Compared with GA algorithm, particle swarm optimization (PSO) algorithm is simple in calculation and requires less parameters adjustment.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

show abstract

Process optimization of high-speed dry milling UD-CF/PEEK laminates using GA-BP neural network

Cited by 51 publications

References 44 publications

Prediction of curing process for thermosetting prepreg compression molding process based on machine learning

Prediction of curing process for thermosetting prepreg compression molding process based on machine learning

Method for an Effective Selection of Tools and Cutting Conditions during Precise Turning of Non-Alloy Quality Steel C45

A machine learning-based approach for product maintenance prediction with reliability information conversion

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