In this paper a 3D finite element (FE) analysis was carried out to study the effects of new variables on predicting the failure mode in shear strengthened reinforced concrete (RC) beams with FRP sheets. Thirty eight specimens were analyzed by considering the effect of beam width, concrete strength, effective height of FRP sheet, FRP thickness, elastic modulus of the FRP sheet and strengthening configuration (U-jacketing, and side bonding). Experimental data of 142 beams collected from previous articles were analyzed to verify the accuracy of the proposed model. The results indicate that the suggested model can calculate the failure mode in shear strengthened with an error less than 4.27 % for debonding failure and error- free for tensile rupture, for beams having side bonding and U-jacketing. Moreover, the proposed model showed higher accuracy in predicting the failure mode in shear strengthened of RC beams with FRP sheets as compared to the existing models.

In this study, a three-dimensional finite-element analysis (FEA) was conducted to study the parameters that affect the maximum flexural fatigue capacity of RC beams strengthened with fiber-reinforced polymer (FRP) sheets. Forty-seven specimens were designed and analyzed by using FEA. Additionally, a fatigue capacity prediction model was developed to reflect the influences of the major parameters, including the fatigue behavior of steel reinforcement, FRP sheets, and FRP-to-concrete bonding; and the influences of minor parameters, such as the yield strength of steel reinforcement, concrete strength, width and thickness of the FRP sheet, and other parameters. The results of experiments on 181 beams reported in the literature were analyzed to verify the accuracy of the proposed model. The mean values of 1.05 and 1.02 and the corresponding coefficients of variation of 17.12 and 16.06% were determined by comparing the calculation results from the proposed model with the experimental data. These results reflect the superior accuracy of the proposed model in predicting the fatigue capacity of RC beams with and without FRP strengthening.

In this study, three-dimensional (3D) finite element (FE) analyses were carried out to study the effects of several variables on the failure modes and ultimate shear capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) sheets. Fifty-eight cases were analyzed by FE modeling. The parameters considered to affect the failure modes and the shear capacity included the beam width, the concrete strength, the height and thickness of the FRP sheet, the elastic modulus of the FRP and the strengthening configuration (complete wrapping, U-jacketing, and side bonding). A model for predicting the failure mode and the shear capacity were proposed on the basis of the results of the parametric analysis. Experimental results for 307 beams collected from previously published work were analyzed to verify the accuracy of the proposed model. The results show that the failure modes of RC beams are affected by the parameters considered and can be predicted by the proposed model. The results also indicate that the proposed model can be used to calculate the shear capacity of RC beams strengthened with FRP sheets and to predict the failure mode with greater accuracy than existing models.

In this paper, a three-dimensional finite-element (FE) analysis was carried out to study the effect of new variables on predicting the ultimate shear capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) sheets. 55 specimens were analyzed by considering the effect of beam width, concrete strength, shear span-to-depth ratio, FRP thickness, and strengthening configuration (completely wrapped, U-jacketing, and side bonding). Experimental results of 274 beams collected from previous published work were analyzed to verify the accuracy of the proposed model. The results show that lateral strain along the top and the bottom of beams are affected by all these variables. This was not considered in previous studies. The results also indicate that the suggested model can calculate the shear capacity of RC beams strengthened with FRP sheets with higher accuracy than existing models, with coefficients of variation reaching 18.9% for side bonding, 17.0% for U-jacketing, and 18.3% for completely wrapped, respectively.

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This paper presents a real time simulation for virtual end milling process. Alyuda NeuroIntelligence was used to design and implement an artificial neural network. Artificial neural networks (ANN’s) is an approach to evolve an efficient model for estimation of surface roughness, based on a set of input cutting conditions. Neural network algorithms are developed for use as a direct modeling method, to predict surface roughness for end milling operations. Prediction of surface roughness in end milling is often needed in order to establish automation or optimization of the machining processes. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The neural network is used to predict surface roughness of the virtual milling machine to analyze and preprocess pre measured test data. The simulation for the geometrical modeling of end milling process and analytical modeling of machining parameters was developed based on real data from experiments carried out using Prolight2000 (CNC) milling machine. This application can simulate the virtual end milling process and surface roughness Ra (µm) prediction graphs against cutting conditions simultaneously. The user can also analyze parameters that influenced the machining process such as cutting speed, feed rate of worktable. Key words: Surface roughness, virtual reality, simulation, surface roughness, virtual end milling process, neural

Abstract Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical

Abstract Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical

Abstract The knowledge of the friction mechanism and hence the distribution of the normal and shear stresses at the chip-tool interface in metal cutting is far from complete. Following the few previous attempts in the literature, the possibility that the boundary layer theory can be invoked to describe the chip flow process at the interface is further investigated. In this paper sticking and sliding friction regions are assumed to exist and the boundary conditions are formulated such that use is made of the boundary layer on a partially mobile solid surface and the flow lines of the chip material in the secondary shear zone are developed. These lines compare well with those obtained by known experimental techniques particularly for the case of large negative rake angle cutting.

ABSTRACT– Multiple regression and adaptive neuro-fuzzy in ference system (ANFIS) were used to predict the surface roughness in the end milling process. Spindle speed, feed rate and depth of cut were used as predictor variables. Generalized bell me mberships function (gbellmf) was adopted during the training process of ANFIS in this study. The pr edicted surface roughness using multiple regression and ANFIS were compared with measured data, the ac hieved accuracy were 91.9% and 94% respectively. These results indicate that the tr aining of ANFIS with the gbellmf is accurate than multiple regression in the prediction of surface roughness.