A prediction model of drilling force in CFRP internal chip removal hole drilling based on support vector regression

Abstract

Drilling force is the main factor affecting the quality of carbon fiber-reinforced polymer (CFRP) holes and tool wear. Choosing appropriate process parameters can effectively control the drilling force and improve the quality of hole making and tool life. This study aimed to accurately predict and effectively control the drilling force during the chip removal hole drilling process in CFRP. First, a CFRP internal chip removal machining drilling force prediction model was derived based on the support vector regression (SVR) theory, and a suitable kernel function and loss function were introduced into the model to improve the prediction accuracy of the model. Second, a drilling experiment of the given type of CFRP material with internal chip removal was designed, and sequential minimal optimization was applied to solve the unknown parameters in the prediction model. The drilling force and tool parameters, suction parameters, and cutting parameter prediction models were constructed for processing a given type of CFRP material. Finally, using the constructed prediction model, the relationship between cutting parameters (speed and feed), tool parameters (drill diameter, peak angle, and relief angle), and suction parameters (negative pressure) and axial force during CFRP internal chip removal hole drilling was predicted and studied. The relationship between the aforementioned parameters and the axial force was in line with the research results of existing studies, and the selection range of tool parameters, cutting parameters, and suction parameters when processing a given CFRP material using an internal chip removal process was also given.