Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models
نویسنده
چکیده
In this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed (V), feed rate (f) and depth of cut (a) while output variable was surface roughness (Ra). Fuzzy logic and ANN models were developed using Matlab Toolbox. Variance analysis was conducted using MINITAB. The predicted values of mean squared errors (MSE) were employed to compare the three models. Results showed that the optimum predictive model is the fuzzy logic model. With small errors (e.g MSE = 0.0173166), the model was considered sufficiently accurate.
منابع مشابه
Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models
The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on De...
متن کاملPredictive modeling of surface roughness and tool wear in hard turning using regression and neural networks
In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface rough...
متن کاملStepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process
Due to the complexity and uncertainty in the process, the soft computingmethods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic andmulti-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of se...
متن کاملSurface Roughness, Machining Force and FlankWear in Turning of Hardened AISI 4340 Steel with Coated Carbide Insert: Cutting Parameters Effects
The current experimental study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (surface roughness, machining force and flank wear) in hard turning of AISI 4340 steel with multilayer CVD (TiN/TiCN/Al2O3) coated carbide insert. Combined effects of cutting parameter (v, f, d) on performance outputs (Ra, Fm and VB) ar...
متن کاملArtificial neural network models for production of nano-grained structure in AISI 304L stainless steel by predicting thermo-mechanical parameters
An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...
متن کامل