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Currently, there is a notable attraction within the industry towards biocomposites, driven by the increasing fascination with natural fiber-reinforced composites (NFRCs). These NFRCs offer remarkable benefits, including cost-effectiveness, biodegradability, eco-friendliness, and favorable mechanical properties. As a result, the manufacturing processes of natural fiber reinforced polymer (NFRP) composites have garnered attention from both industrial professionals and scientists. The emergence of these eco-friendly materials in the automotive and aerospace industries has sparked interest in understanding their production techniques. However, the machining processes of NFRP composites pose significant challenges due to the complex structure of natural fibers, necessitating thorough studies to address these issues effectively. This research paper presents a comprehensive investigation on surface roughness during the milling process of Alfa/epoxy biocomposites. A set of 100 experimental trials was conducted to test the surface roughness, and analysis of variance (ANOVA) was used to assess the impact of cutting parameters and chemical treatment on surface quality. To develop a predictive model for surface roughness, a hybrid approach called ANN-GA (artificial neural networks-genetic algorithms) is proposed in this research. This approach combines ANN and GA to determine an optimal neural network architecture. The performance of the ANN-GA model is compared to the Levenberg–Marquardt backpropagation (LM) algorithm. ANOVA results show that the feed per revolution have a significant influence on surface roughness, followed by the chemical treatment of fibers, while machining direction has a smaller effect. The ANN-GA model demonstrates good accuracy in surface roughness prediction compared to the LM algorithm. [ABSTRACT FROM AUTHOR]

New biocomposites are increasingly being developed and applied in engineering and manufacturing. The reinforcement type used in biocomposite is one of the major factors that greatly affect the machinability of biocomposite. This study investigated the drilling of two biocomposites (polypropylene-based biocomposite reinforced with biocarbon M1 (30 wt% biocarbon + PP/POE/MAPP) and chopped miscanthus fibers M2 (30 wt% miscanthus + PP/POE/MAPP)). A full factorial design was used for this study. The effects of drilling parameters and reinforcement types on surface roughness, thrust force, specific cutting energy, fine particles, and ultrafine particle generation were measured and analyzed. The results show that the thrust force and specific cutting energy during drilling of biocomposite M1 is bigger than that of biocomposite M2 under the same cutting conditions. However, the surface roughness and fine dust emitted when drilling biocomposite M1 are smaller than those from biocomposite M2, respectively. The reinforcement types of biocomposite, cutting parameters and drill diameter greatly affected fine particles emission. Meanwhile, the cutting conditions are not statistically significant for ultrafine particle generation during dry drilling of both biocomposites. [ABSTRACT FROM AUTHOR]