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Detection of tool wear and breakage during machining operations is one of the major problems in control and optimization of the automatic machining process. In this study, the relationship between tool wear with vibration in the two directions, one in the machining direction and the other perpendicular to machining direction was investigated during face milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable sandvik insert and ck45 work piece were used in the experiments. Tool wear was measured by a microscope. It was observed that there was an increase in vibration amplitude with increasing tool wear. In this study adaptive neuro - fuzzy inference systems (ANFIS) and multi-layer perceptron neural network (MLPNN) were implemented for classification of tool wear. In this study for the first time, five different states of tool wear was used for accurate tool wear classification. Also to accuracy and speed of the network Principle Component Analysis (PCA) was implemented. Using PCA, the input matrix size was reduced to an acceptable order causing more efficient networks. ANFIS and MLP were trained using feature vectors extracted from the spectrum frequency and time signals. The results showed that for 86 final measurements, the ANFIS and MLP networks were successful in classifying different tool wear state correctly for 91 and 82 percent, respectively. ANFIS due to its high efficiency in diagnosing tool wear and breakage can be proposed as proper technique for intelligent fault classification.

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This research aims to describe a novel model, namely Hybrid Adaptive-Neuro Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), for predicting corrosion rate of 3C steel considering different marine environment factors. In the present research, five parameters (temperature, dissolved oxygen, salinity, pH, and oxidation–reduction potential) were used as input variables, with corrosion rate being the only output variable. In the proposed hybrid ANFIS-PSO model, the PSO served as a tool to automatically search for and update optimal parameters for the ANFIS, so as to improve generalizability of the model. Eeffectiveness of the hybrid model was then compared those to two other models, namely Adaptive-Neuro Fuzzy Inference System–Genetic Algorithm (ANFIS-GA) and Support Vector Regression (SVR) models, by evaluating their results against the same experimental data. The results showed that the proposed hybrid model tends to produce a lower prediction error than those of ANFIS-GA and SVR with the same training and testing datasets. Indeed, the hybrid ANFIS-PSO model provides engineers with an applicable and reliable tool to conduct real-time corrosion prediction of 3C steel considering different marine environment factors.