Velmurugan, Samikkannu, Raja Murugan, Thavidan Vellaiyan, Subravel, Visvalingam. (1405). Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach. هنرهای کاربردی, 13(2), 415-426. doi: 10.22075/macs.2025.36924.1804
Samikkannu Velmurugan; Thavidan Vellaiyan Raja Murugan; Visvalingam Subravel. "Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach". هنرهای کاربردی, 13, 2, 1405, 415-426. doi: 10.22075/macs.2025.36924.1804
Velmurugan, Samikkannu, Raja Murugan, Thavidan Vellaiyan, Subravel, Visvalingam. (1405). 'Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach', هنرهای کاربردی, 13(2), pp. 415-426. doi: 10.22075/macs.2025.36924.1804
Velmurugan, Samikkannu, Raja Murugan, Thavidan Vellaiyan, Subravel, Visvalingam. Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach. هنرهای کاربردی, 1405; 13(2): 415-426. doi: 10.22075/macs.2025.36924.1804

Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach

Mechanics of Advanced Composite Structures
مقاله 13، دوره 13، شماره 2 - شماره پیاپی 28، بهمن 2026، صفحه 415-426    اصل مقاله (799.13 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.36924.1804
نویسندگان
Samikkannu Velmurugan* 1؛ Thavidan Vellaiyan Raja Murugan1؛ Visvalingam Subravel2
1Department of Manufacturing Engineering, Annamalai University, Chidambaram, 608002, India
2Department of Mechanical Engineering, Government College of Engineering, Thanjavur, 613402, India
چکیده
The study investigates the prediction and parametric effects in drilling of Prosopis juliflora fiber (PJF)-reinforced epoxy resin hybrid composite using a combined Taguchi-artificial neural network (ANN) approach. The composite was prepared via hand lay-up technique with natural reinforcement including vetiver fiber (VF) and coir pith (CP). The effects of drill bit diameter (DBD), spindle speed (SS), and feed rate (FR) on thrust force (TF) and surface roughness (SR) were evaluated through a full factorial design. An ANN model developed using a feedforward backpropagation algorithm successfully predicted the responses. Analysis of variance (ANOVA) results revealed that the regression coefficient (R2) for TF and SR were 96.39% and 95.54%, respectively. The DBD and FR were identified as the most significant parameters influencing TF and SR, both significant at the 95% confidence level (p<0.05). The regression plot exhibited a strong correlation (R=0.9883) between the predicted and actual values, while the ANN model achieved a mean squared error (MSE) of 0.089758 within 2 epochs. TF and SR increased with higher DBD and FR but decreased with an increased SS, as indicated by the main effect plots. Scanning electron microscope (SEM) revealed drilling-induced mechanisms, including fiber pullout, delamination, matrix cracking, matrix debonding, and matrix deformation. The findings demonstrate enhanced machining performance, offering potential for industrial applications and future research on bio-composite materials.
کلیدواژه‌ها
Taguchi؛ Artificial Neural Network (ANN)؛ Hand lay-up technique؛ Surface Roughness (SR)؛ Scanning Electron Microscope (SEM)
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