Hybrid evolutionary optimization for nutraceutical manufacturing processes

Liu, Tung-Kuan; Chou, Yu-Cheng; Wen, Yuan-Tang

doi:10.1007/s10845-015-1079-8

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In comparison to wavelet analysis, the Empirical Mode Decomposition (EMD) has seen less use in recent TCM studies. However, its combination with WT and other methods has shown promise in detecting tool wear (Babouri et al [32], Li et al [33], Liu et al [34]). This suggests that while the WPT is a preferred method, alternative techniques like EMD can still contribute to the field.…”

Section: Introductionmentioning

confidence: 99%

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

Chen,

Lin,

Zhang

et al. 2024

Sensors

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Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch’s method complemented by time–frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

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Section: Introductionmentioning

confidence: 99%

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

Chen,

Lin,

Zhang

et al. 2024

Sensors

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“…erefore, the automatic identification of defective capsules is needed during the production process. In recent years, numerous online capsule detection technologies based on computer vision have been applied [3,6,7]. For example, Qi and Jiang proposed a capsule defect detection method based on the hierarchical support vector machine to extract capsule defects in a real-time capsule defect recognition system [2].…”

Section: Introductionmentioning

confidence: 99%

Identifying Capsule Defect Based on an Improved Convolutional Neural Network

Zhou

et al. 2020

Shock and Vibration

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Capsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) algorithm. The algorithm is used for defect detection and classification in capsule production. Defective and qualified capsule images in the actual production are collected as samples. Then, a deep learning model based on the improved CNN is designed to train and test a capsule image dataset and identify defective capsules. The improved CNN algorithm is based on regularization and the Adam optimizer (RACNN), on which a dropout layer and L2_regularization are added between the full connection and the output layer to solve the overfitting problem. The Adam optimizer is introduced to accelerate model training and improve model convergence. Then, cross entropy is used as a loss function to measure the prediction performance of the model. By comparing the results of RACNN with different parameters, a detection method based on the optimal parameters of the RACNN model is finally selected. Results show a 97.56% recognition accuracy of the proposed method. Hence, this method could be used for the automatic identification and classification of defective capsules.

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“…In GA, the fitness function as the objective function is designed according to concrete problems and demands, and the optimal solution is searched in a preset range of parameters. Here, the fitness function is defined as follows: (Liu et al, 2017).…”

mentioning

confidence: 99%

Variable snap-off study for 3D additive screen printing

Wang

Liu

et al. 2020

IJCST

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Purpose In 3D additive screen printing with constant snap-off, the inhomogeneous screen counterforce will influence the printing force and reduce the printing quality. The purpose of this paper is to study the relationship between scraper position, snap-off and screen counterforce and develop a variable snap-off curve for 3D additive screen printing to improve the printing quality. Design/methodology/approach An experiment was carried out; genetic algorithm (GA) optimization theoretical model, backpropagation neural network regression model and least square support vector machine regression model were established to study the relationship between scraper position, snap-off and screen counterforce. The absolute errors of counterforce of three models with the experiment results were less than 1.5 N, which was tolerated and the three models were considered valid. The comparison results showed that GA optimization theoretical model performed best. Findings The results suggest that GA optimization theoretical model performed best to represent the relationship, and it was used to develop a variable snap-off curve. With the variable snap-off curve in 3D additive screen printing, the inhomogeneous screen counterforce was weakened and the printing quality was improved. Originality/value In printing production, the variable snap-off curve in 3D additive screen printing helps improve the printing quality; this study is of prime importance to the 3D additive screen printing.

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Hybrid evolutionary optimization for nutraceutical manufacturing processes

Cited by 4 publications

References 20 publications

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

Identifying Capsule Defect Based on an Improved Convolutional Neural Network

Variable snap-off study for 3D additive screen printing

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