Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber-reinforced composites

The aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber-reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF-reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber-reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine-tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications. Optimization and ANN Prediction in abrasive water jet machining process. image