Laser processing characteristics of PCD tool and modeling analysis

Abstract

Abstract Through in-depth analysis of the experimental details and forming patterns of the nanosecond laser ablation polycrystalline diamond (PCD) textured tool processing system, this study explores the microscopic morphology and dimensions of micro-pits texture on the surface of PCD tools influenced by defocus amount, laser power, and pulse frequency. Experimental results indicate that the micro-pit textures generated under different parameters exhibit diversity, including rounded structure, fragments, recast layers, and heat-affected zones. The diameter and depth of micro-pits are comprehensively affected by defocus amount, laser power, and pulse frequency, showing complex patterns. After a thorough analysis of the effects of each parameter on the texture morphology, an artificial neural network (ANN) model is introduced for the prediction of micro-pit dimensions. Through model training and optimization, accurate predictions of micro-pit diameter and depth are obtained. In comparison to traditional regression models, the ANN model demonstrates outstanding predictive performance, validating its applicability in complex machining processes. This study not only provides a profound understanding of the processing patterns of PCD textured tools but also offers an effective predictive model for the optimization and control of similar future machining processes.