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The International Journal of Advanced Manufacturing Technology

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14.10.2022 | ORIGINAL ARTICLE

Drilling performance prediction of HDPE/Washingtonia fiber biocomposite using RSM, ANN, and GA optimization

verfasst von: Ahmed Belaadi, Messaouda Boumaaza, Hassan Alshahrani, Mostefa Bourchak, Mohammad Jawaid

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 5-6/2022

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Abstract

Polymeric biocomposites are emerging as a convenient alternative for traditional materials used in light-weight structural applications. Many studies on the machining of biocomposites were published in recent years. However, their number remains limited compared to the number of studies on the machining of glass and carbon fiber–reinforced composites. Therefore, drilling of Washingtonia filifera (WF) fiber-reinforced high-density polyethylene (HDPE) biocomposites was studied in this research by varying various factors, such as drill diameter (d), feed rate (f), and spindle speed (N) using full factorial design (L₂₇). Response surface methodology (RSM) was applied for the drilling experiment and was used in conjunction with artificial neural network (ANN) in mathematical modeling of the drilling operation parameters. The results showed excellent agreement between experimental data and RSM/ANN predictions. Hence, the developed biocomposite HDPE/WF can be used in the polymer field to improve overall product performances. Furthermore, the drilling parameter optimization results obtained by the genetic algorithm (GA) combined with ANN are almost similar to those of the desirability function (DF) of RSM, especially f = 50 mm/min, N = 806 rev/min, and d = 5 mm.

Vorheriger Artikel Machining characteristics of various powder-based additives, dielectrics, and electrodes during EDM of micro-impressions: a comparative study

Nächster Artikel Deformation characteristics and forming force limits of multi-point forming with individually controlled force–displacement

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Titel: Drilling performance prediction of HDPE/Washingtonia fiber biocomposite using RSM, ANN, and GA optimization
verfasst von: Ahmed Belaadi
Messaouda Boumaaza
Hassan Alshahrani
Mostefa Bourchak
Mohammad Jawaid
Publikationsdatum: 14.10.2022
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 5-6/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-022-10248-6

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