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Microchannel fabrication and metallurgical characterization on titanium by nanosecond fiber laser micromilling
摘要： Laser micromilling technique is used to manufacture microchannel on metals and nonmetals. Microfeatures ≤100 μm are still challenging for fabrication by common methods. Nanosecond fiber laser micromachining has become more popular owing to its prospective implementation in laser micromilling. Microchannel application relies on its geometric dimension, profile, and surface quality. In this study, an attempt was made to explore the impact of process parameters scanning times, scanning velocity, pulse repetition rate, and assist gas pressure on top kerf width, taper, surface roughness, and metal removal rate in laser micromilling experimentally. Microchannel width varied between 45.5 and 70.9 μm. A regression model has been developed for each response. ANOVA (analysis of variance) has been carried out to remove insignificant parameters. Thermal stress analyzed by surface cracks inside microchannel by Scanning Electrone Microscopy (SEM) images. Higher PRR, lower no. of scans, higher scanning speed and high air pressure found suitable for lesser surface cracks. Redeposition observed at slower scanning velocity and minimum scanning times. Oxidation zone from boundary of channel varies between 37 and 58 μm. Oxide formed on Ti surface which increases oxygen content toward center of channel from 51.08 to 76.22% compared to outside surroundings.
关键词： oxidized zone,MRR,kerf width,LMM,surface cracks,taper,surface roughness,recast layer
更新于2025-09-19 17:13:59
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Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques
摘要： Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.
关键词： Laser drilling,ANFIS,Genetic programming,Stainless steel,Artificial intelligence,Ti6Al4V,Surface cracks
更新于2025-09-12 10:27:22