Artificial neural network modelling of Nd:YAG laser microdrilling on titanium nitride—alumina composite

Abstract

Selection of machining parameter combinations for obtaining optimum circularity at entry and exit and hole taper is a challenging task in laser microdrilling owing to the presence of a large number of process variables. There is no perfect combination of parameters that can simultaneously result in higher circularity at entry and exit and lower hole taper. The current paper attempts to develop a strategy for predicting machining parameter settings for the generation of the maximum circularity at entry and exit and minimum hole taper. An artificial neural network (ANN) is used for process modelling of laser microdrilling of titanium nitride-alumina composite and a feed-forward back-propagation network is developed to model the machining process. The model, after proper training, is capable of predicting the response parameters as a function of five different control parameters. Experimental results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements. The developed model is found to be unique, powerful, and flexible.

Laser Beam MicroMachining (LBMM) – A review

“…For multiobjective optimization of the process for minimum surface roughness and minimum volume error, Particle Swarm Optimization (PSO) technique was used and it was observed that the proposed model and optimization technique can predict the optimum parameters accurately. Similarly, Biswas et al [191] have also developed neural network model to optimize the geometric features of micro-drilled hole in TiN-Al 2 O 3 .…”

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

“…In the field of laser cutting, most of the time the optimal cutting conditions are determined using the Taguchi method (Prajapati et al, 2013) and by coupling response surface models (Sivarao et al, 2013) and ANNs models with different optimization and metaheuristic algorithms such as particle swarm optimization (Ciurana et al, 2009), genetic algorithm (GA) (Tsai et al, 2008), and simulated annealing (Chaki & Ghosal, 2011). The open literature reveals several research attempts based on ANNs such as for modeling and optimization of laser micromachining process (Ciurana et al, 2009;Biswas et al, 2010;Dhara et al, 2008;Dhupal et al, 2007), selection of optimal laser cutting parameters through integration of ANNs with GA (Tsai et al, 2008;Ghoreishi & Nakhjavani 2008), development of a prediction model through integration with the Taguchi method (Yang et al, 2012) and parametric modeling and optimization of lasox cutting (Chaki & Ghosal, 2011). The ANNs are the learning algorithms and mathematical models, which imitate the information processing capability of human brain and can be applied to non-linear and complex data, even if the data are imprecise and noisy (Raja et al, 2012).…”

Multi-objective optimization of cut quality characteristic in CO2 laser cutting stainless steel

“…In laser cutting, the material is melted or evaporated by focusing the laser beam on the workpiece surface and coaxial jet of an assist gas removes the evaporated and molten material from the affected zone. Laser cutting is a high energy-density process that works quickly on the complex shapes, and is applicable to almost any type of material, generates no mechanical stress on the workpiece, reduces waste, provides ecologically clean technology, and has the ability to do work in the micro range [1]. Compared with other conventional machining processes, laser cutting removes much less material, involves highly localized heat input to the workpiece, minimizes distortion, and offers no tool wear [2].…”

“…[1,2,12,13,14,15]. The multi-objective optimization is done by combining the multiple objectives into single objectives through the use of weights or utility function.…”