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.

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.

ABSTRACT End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity. Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining - ResearchGate. Available from: http://www.researchgate.net/publication/265785871_Cutting_force-based_adaptive_neuro-fuzzy_approach_for_accurate_surface_roughness_prediction_in_end_milling_operation_for_intelligent_machining [accessed Jun 18, 2015].

ABSTRACT End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity. Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining - ResearchGate. Available from: http://www.researchgate.net/publication/265785871_Cutting_force-based_adaptive_neuro-fuzzy_approach_for_accurate_surface_roughness_prediction_in_end_milling_operation_for_intelligent_machining [accessed Jun 18, 2015].

ABSTRACT End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity. Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining - ResearchGate. Available from: http://www.researchgate.net/publication/265785871_Cutting_force-based_adaptive_neuro-fuzzy_approach_for_accurate_surface_roughness_prediction_in_end_milling_operation_for_intelligent_machining [accessed Jun 18, 2015].

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