Hybrid ANOVA-ANN modeling and optimization of pulsed wave fiber laser microcutting parameters for machining Ni-Cr-Mo superalloy

Abstract A non-conventional form of versatile machining process, particularly for machining hard and lightweight material is pulsed wave fiber laser micromachining (FLMM). In present work, pulse mode fiber laser machine was used to cut micro hexagonal profiles on Ni-Mo-Cr superalloy considering laser power, scanning speed, pulse frequency and number of passes as input parameters also analyses the output responses such as Kerf Width (KW), Heat Affected Zone (HAZ), Surface Roughness (Ra) and Material Removal Rate (MRR). Multi-objective optimization was performed through ANOVA and Artificial Neural Network (ANN). The 3D surface roughness analyser is used to check machined micro-hexagonal profiles. An MRR of 0.0959 mm 3 /s, kerf width of 24.7 mm, HAZ of 26.1 mm, and surface roughness of 0.084 mm were achieved at the optimal machining conditions. ANN model is developed to validate experimental findings and effectively used to predict the output responses. This work demonstrates the effectiveness of pulsed mode fiber laser micromachining of Ni-Mo-Cr superalloys with useful insights for aerospace, medical and microelectronic sectors.