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Samikkannu, Velmurugan, Thavidan Vellaiyan, Raja Murugan, Visvalingam, Subravel. (1404). Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach. هنرهای کاربردی, (), -. 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, , , 1404, -. doi: 10.22075/macs.2025.36924.1804
Samikkannu, Velmurugan, Thavidan Vellaiyan, Raja Murugan, Visvalingam, Subravel. (1404). 'Prediction and Parametric Effect in Drilling of Natural Fiber Laminate Using Combined Taguchi- Artificial Neural Network Approach', هنرهای کاربردی, (), pp. -. 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, 1404; (): -. 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
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 01 آذر 1404    اصل مقاله (781.01 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.36924.1804
نویسندگان
Velmurugan Samikkannu* 1؛ Raja Murugan Thavidan Vellaiyan2؛ Subravel Visvalingam3
1Department of Manufacturing Engineering, Annamalai University, Chidambaram-608002, India
2Department of Manufacturing Engineering, Annamalai University, Chidambaram, 608002, India
3Department of Mechanical Engineering, Government College of Engineering, Thanjavur, 613402, India
تاریخ دریافت: 29 بهمن 1403،  تاریخ بازنگری: 15 مهر 1404،  تاریخ پذیرش: 01 آذر 1404 
چکیده
The study investigates the prediction and parametric effects in drilling of prosopis juliflora fiber (PJF)-reinforced epoxy resin hybrid composite using 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 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 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 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 their 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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