Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

Soepangkat, Bobby Oedy Pramoedyo; Pramujati, Bambang; Effendi, Mohammad Khoirul; Norcahyo, Rachmadi; Mufarrih, Am.

doi:10.1007/s12541-019-00017-z

Cited by 32 publications

(16 citation statements)

References 23 publications

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“…Duan et al [93] developed a multi-scale coarse-grained model using artificial neural network, a machine learning algorithm aided with particle swarm optimization, which was used to understand the inherent mechanisms of epoxy-based composites, facilitating the composite design with better performance. Various researchers have used machine learning algorithms to optimize the process parameters and characteristics like composite composition, delamination, surface roughness, thrust force, material properties and the overall structural design [94][95][96][97][98][99]. Li et al [100] used a machine learning based iterative method to optimize the synthesis process of short fibers considering all the quantitative and qualitative objectives.…”

Section: Prediction Optimization and Uncertainty Quantificationmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Prediction Optimization and Uncertainty Quantificationmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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“…Camposeco-Negrete [12] uses a robust design technique to control the results and contributions of four machining parameters on the above-mentioned response variables in wire-cut EDM. Soepangkat et al [13] propose a grey fuzzy analysis and BPNN-based GA to control and predict the optimal parameters in the drilling KFRP. Venkata and Murthy [14] combine predictive models such as response surface methodology, artificial neural networks and support vector machine to predict the surface roughness and root mean square of work piece vibration in the boring process.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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“…PCA is a dimension reduction method, which can reduce the negative impact of redundant information [27]. Further, the initial weight and threshold of network are optimized by genetic algorithm (GA), which can promote the fitting accuracy and speed of network [28]. erefore, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is constructed as the risk prediction model for GICP.…”

Section: Introductionmentioning

confidence: 99%

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Tao

Yong

Yang

et al. 2022

Mathematical Problems in Engineering

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Government-invested construction project (GICP) has a great significance to social and economic development but suffered many risks due to its large scale, huge investment, and long construction period. The risks in GICP are complex so as to lead the project to failure; it is extremely urgent to take the risk management of GICP. This study establishes a risk early-warning framework to help the managers to understand the risk threat in advance, which supports them to make proper management strategies for the risk control. The whole framework can be concluded as three parts: information collection, data processing, and result prediction. Firstly, the 16 risk factors of GICP are identified. To express the hesitance of human decision and reduce the information loss in quantification, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are used to collect the experts’ linguistic term and transform them into numerical value. And then, these inputs are simplified into five factors based on principal component analysis (PCA), decreasing the impact of redundancy to risk early-warning. Meanwhile, the warning level is divided based on K-means, which avoids the subjectivity of experience decision. Further, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is used to complete the simulation of risk value. The 75 groups of questionnaire data are used to train the network and the 10 groups are used as test set. The validation of the proposed framework has been verified with an average relative error in 7.2% and the average absolute error in 3.91. Finally, corresponding suggestions to prevent and control the different risks in GICP are put forward.

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Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches

Cited by 32 publications

References 23 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

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