Multi-objective optimization of vibration assisted electrical discharge drilling process using PCA based GRA method

Pandey, Gaurav Kumar; Yadav, Sanjeev Kumar Singh

doi:10.1016/j.matpr.2020.02.561

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The hold-out procedure is employed by the SVM to determine the combination 24 : parameter C and radial basis function kernel parameter g. The grid-search strategy is used to determine the two parameters in SVM. Wang et al 25 suggested trying exponentially growing sequencies of C (2 25 , 2 23 , ., 2 15 ) and g (2 215 , 2 213 , ., 2 3 ) to identify good input parameters when the grid-search method is adopted. The different exponential values of C and g are given to determine the combination (Table 1).…”

Section: Dynamic Stb Pca Methodsmentioning

confidence: 99%

“…For reduction of signal dimensionality, the selection of key parameters or features has been applied to PCA-based techniques. For example, Pandey and Yadav 15 optimized process parameters for vibration-assisted electrical discharge drilling by employing PCA-based gray relational analysis. Wang et al 16 fused multidimensional features on the basis of PCA to comprehensively characterize the operation state of rolling bearings.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic ST-based PCA method for adaptive data detection

Lin

2022

Advances in Mechanical Engineering

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This study developed a dynamic principal component analysis (PCA)-based algorithm for adaptive data detection. The algorithm employs suitable STs on the basis of various data to achieve high accuracy. The scree test (ST) has long been criticized for its subjectivity because no standard applies for retaining the correct number of components or factors when identifying various types of data. This article proposes a novel dynamic ST-based (STB) PCA method wherein a suitable ST is selected in using a support vector machine (SVM) for determining the correct number of components in data detection. The dynamic STB PCA can be employed as a solution to effectively detect various types of data. The proposed detection system can bridge the gap between input data and suitable STs for solving problems encountered when implementing data detection. The experimental results show that the STB PCA provides a ST-selection tool for automatically selecting the most suitable STs, and effectively detected various data using the STs. In the data detection, the proposed method outperforms existing PCA methods that do not consider suitable STs.

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Section: Dynamic Stb Pca Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic ST-based PCA method for adaptive data detection

Lin

2022

Advances in Mechanical Engineering

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Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

Sana,

Khan,

Farooq

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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In the present era dominated by Industry 4.0, the digital transformation and intelligent management of industrial systems is significantly important to enhance efficiency, quality, and the effective use of resources. This underscores the need for a framework that goes beyond merely boosting productivity and work quality, aiming for a net-zero impact from industrial activities. This research introduces a comprehensive and adaptable analytical framework intended to bridge existing gaps in research and technology within the manufacturing sector. It encompasses the essential stages of using artificial intelligence (AI) for modelling and optimizing manufacturing systems. The effectiveness of the proposed AI framework is evaluated through a case study on electric discharge machining (EDM), concentrating on optimizing the electrode wear rate (EWR) and overcut (OC) for aerospace alloy Inconel 617. Utilizing a comprehensive design of experiments, the process modelling through an artificial neural network (ANN) is carried out, accompanied by careful fine-tuning of hyperparameters throughout the training process. The trained models are further assessed using an external validation (Valext) dataset. The results of the sensitivity analysis indicated that the surfactant concentration (Sc) has the highest level of influence, accounting for 52.41% of the observed influence on the EWR, followed by the powder concentration (Cp) with a contribution of 33.14%, and the treatment variable with a contribution of 14.43%. Regarding OC, Sc holds the highest percentage significance at 72.67%, followed by Cp at 21.25%, and treatment at 6.06%. Additionally, parametric optimization (PO) shows that EWR and OC overcome experimental data by 47.05% and 85.00%, respectively, showcasing successful performance optimization with potential applications across diverse manufacturing systems.

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