Laser-induced graphene (LIG) is an emerging technique for producing few-layer graphene or graphene-like material that has recently received increasing attention, due to its unique advantages. Subsequently, a variety of lasers and materials have been used to fabricate LIG using this technique. However, there is a lack of understanding of how different lasers (wavelengths) perform differently in the LIG conversion process. In this study, the produced LIG on polyimide (PI) under a locally water-cooled condition using a 10.6 μm CO₂ infrared laser and a 355 nm ultraviolet (UV) laser are compared. The experimental investigations reveal that under the same UV and CO₂ laser fluence, the ablation of PI show different results. Surface morphologies with micron-sized and nanometer pores were formed by the UV laser under different laser fluences, whereas micron-sized pores and sheet structure with fewer pores were produced by the CO₂ laser. Energy dispersive spectrometry and three-dimensional topography characterization indicate that the photochemical effects were also involved in the LIG conversion with UV laser irradiation. It is also observed through experiments that the photothermal effect contributed to the formation of LIG under both lasers, and the LIG formed on PI substrates by the CO₂ laser showed better quality and fewer layers.

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al₂O₃-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

Equal channel angular rolling (ECAR) is a severe plastic deformation (SPD) process in order to achieve ultrafine-grained (UFG) structure. In this paper, the mechanical properties of ECAR process using artificial neural network (ANN) and nonlinear regression have been illustrated. For this purpose, a multilayer perceptron (MLP) based feed-forward ANN has been used to predict the mechanical properties of ECARed Al5083 sheets. Channel oblique angle, number of passes and the route of feeding are considered as ANN inputs and tensile strength, elongation and hardness are considered as the outputs of ANN. In addition, the relationship between input parameters and mechanical properties were extracted separately using nonlinear regression method. Comparing the outputs of models and experimental results shows that models used in this study can predict and estimate mechanical properties appropriately. Where, the performance of ANN model is better than the correlations to predict mechanical properties. Finally, the developed outputs of neural network model are used to analyze the effects of input parameters on tensile strength, elongation and hardness of ECARed Al5083 sheets.