An Effective PSO-LSSVM-Based Approach for Surface Roughness Prediction in High-Speed Precision Milling

Li, Bo; Tian, Xitian

doi:10.1109/access.2021.3084617

Cited by 28 publications

(11 citation statements)

References 21 publications

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“…After obtaining the dual response surface models for and from A/EKO-DRA using the actual response as a reference 43 , the optimal parameter levels were determined by using Copeland and Nelson’s dual response algorithms in ( 3 )–( 5 ). The maximum deviation ( ), which is calculated from the response mean ( ), and the specified target value ( ) are set at 0.05 and 0.059, respectively.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

Luangpaiboon

Juttijudata

2023

Sci Rep

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This study proposes a novel hybrid approach, called Adaptive/Elevator Kinematics Optimization algorithm based on dual response algorithm (A/EKO-DRA), to enhance the robust parameters estimation and design of the plaster milling process. The A/EKO-DRA method reduces variability while maintaining the desired output target, thereby minimizing the impact of variance on the expected stucco combined water. The performance of the A/EKO-DRA is compared with conventional processes through numerical examples and simulations. The results show that the A/EKO-DRA method has the lowest mean absolute errors among other methods in terms of parameter estimation, and it achieves the response mean of 5.927 percent, which meets the target value of 5.9 percent for industrial enclosures, with much reduction in the response variance. Overall, the A/EKO-DRA method is a promising approach for optimizing the plaster milling process parameters.

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Section: Numerical Results and Analysismentioning

confidence: 99%

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

Luangpaiboon

Juttijudata

2023

Sci Rep

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“…In the existing TCM system based on a data-driven model, according to whether the data processing model needs training data, it is mainly divided into a supervised TCM system and an unsupervised TCM system. A supervised TCM system requires more or less teacher data to train model parameters, such as Hidden Markov Model [9,10], Neural Network [11][12][13], SVM [14,15], Deep Learning [16,17], etc. The Hidden Markov Model (HMM) is a Markov chain and a production model.…”

Section: Introductionmentioning

confidence: 99%

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Zhong

Liao

et al. 2023

Applied Sciences

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Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.

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“…The most typical ANN architectures for analysis and prediction of the above-mentioned output machining process parameters are back-propagation neuron network (BPNN) [ 74 , 76 , 77 ], conventional neuron network [ 78 ], and multi-layer perceptron (MLP) [ 72 , 75 , 79 ]. The ANN analysis is ensured by the Levenberg–Marquardt backpropagation algorithm [ 72 , 73 , 76 , 77 , 79 ] and scaled conjugate gradient training algorithm [ 75 ], particle swarm optimization algorithm [ 80 ], and Bayesian regularization [ 74 ]. The application of ANN for analysis of the experimental results of CFRP drilling was dedicated to predicting tool wear based on thrust force analysis [ 25 , 81 ].…”

Section: Introductionmentioning

confidence: 99%

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

et al. 2022

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Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

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An Effective PSO-LSSVM-Based Approach for Surface Roughness Prediction in High-Speed Precision Milling

Cited by 28 publications

References 21 publications

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

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