Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

This paper presents an approach of linking finite element method with artificial neural network to predict J-Integral parameter in desirable airfoil condition. Finite Element (FE) and Artificial Neural Network (ANN) have been employed for the purpose. In other words, a prediction of finite element results has been done using ANN. Ultimately results of two methods have been compared for different cases. Wing fracture is a well-known problem of the planes which depends on various parameters. The J-integral is a vital parameter in evaluations of structure fracture phenomena. On the other hand residual stresses play an influential role in fracture formation. In the current work, effect of residual stresses and crack depth on J-Integral has been investigated in a standard NACA0012-34 airfoil. As will be seen, residual stresses and crack depth influence J-Integral values. It also will be shown that predictions of ANN method are in a good agreement with those obtained by finite element method.

Buckling is one of the most complicated concepts in mechanical engineering. Buckling often happens by compressive loads on thin structures. Thermal gradient between two ends of a column may cause a deflection in it. This will add an extra deformation to the one provided by compressive loads on the column. This phenomenon occurs when two ends of the column are at different temperatures, which can be seen at various structures. Because of the considered temperature gradients, the critical load of the column will decrease. In the current paper, various columns are modeled and the effect of thermal gradient and compressive load and other parameters on the Bi-material columns are studied. In other words, influences of compressive load and temperature gradient on critical load of Bi-material columns with interface crack are investigated. Effect of change in each parameter on critical load of column and crack opening was investigated. First, the thermal gradient was only applied to the model and in the next step; only the effect of mechanical loading was studied. Furthermore, artificial neural network (ANN) was used to extend the results to a bigger range of temperature conditions through the columns. Based on the results, ANN and finite element results are in a good agreement and the thermal effects may have a significant role in buckling of the column.

The evolving technology has pushed machine learning techniques to replace human smartness. A machine learning model is capable of learning and replicating like our brain. This approach of data-driven model is implemented to predict the cutting force in machining of Ti6Al4V. Titanium alloys are commonly used in high strength applications due to their excellent properties. These same properties make the machining of the titanium alloy complicated. An attempt has been made for finding the cutting force under minimum quantity lubrication (MQL). MQL is a sustainable manufacturing-based lubrication system. Taguchi’s approach was used to attain a full factorial design for combination of different parameters. Accordingly, a neural network (NN) model was developed which was capable of predicting cutting forces based on the trained model. The proposed model could be implemented to find optimal parameters in shortest duration, thereby eliminating the need for experimental computations.