Artificial neural networks for vibration based inverse parametric identifications: A review

Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes

On-line tool wear estimation in CNC turning operations

In order to prevent tool breakage and resultant decrease in productivity in unmanned turning operations, many researchers have attempted to develop tool wear estimation and classification models. These include neural network models, fuzzy logic models and working scenario for quantitative models. The worn tools need to be replaced before their wear exceeds the allowed limits. Normally, cutting forces, AErms and cutting conditions including cutting speed, feed rate, rake angle and depth of cut are employed as inputs in these models. In the recent past, however, many researches have focused on flank wear prediction and off-line tool wear prediction systems. Additionally, the accuracy of tool wear prediction for these models needs to be increased. Therefore, in this research, a new on-line tool wear estimation system having higher accuracy for estimating the length of flank wear and the maximum depth of crater wear in CNC turning operations is developed

Optimization of Design for Air Gap Sensor Using the Response Surface Methodology

In Hard Disk Drive (HDD) manufacturing, there is always a concern about the cutting defects that are caused by residual cutting chips. Only a small amount of 10 μm chips (act as the air gap) can cause the workpiece to tilt and shift from the correct position, and thus affect the dimension of the workpiece (mainly the Base HDD). For this reason, researchers adapted the adjustable micrometer as a simulation device that resembles the air gap for the design of the Air Gap Sensor Module. The design of experiments using response surface methodology will be studied to confirm the appropriate factors of the prototype. This study reports the optimization of the main factors that affect Air Gap Sensor Module condition: Air Nozzle Diameter 2.303 mm, Air Pressure 0.1 MPa, and Sampling Time 645 ms, which has a high square of the coefficient correlation (R-squared = 99.0%) with a close relationship between gap distance and air pressure. The relationship between these variables is mostly linear. The R-squared error percentage of actual value is less than 0.93% compared to predicted value. The mathematical model results and experimental values were consistent and able to predict response variables. The Air Gap Sensor Module can provide the measurement results in micron accuracy and displays light and beep to confirm as acceptable or reject gap conditions with the uncertainty of measurement ± 0.001 mm

A Computer Algorithm for Flank and Crater Wear Estimation in CNC Turning Operations

In order to increase the productivity of turning processes, several attempts have been made in the recent past for tool wear estimation and classification in turning operations. The tool flank and crater wear can be predicted by a number of models including statistical, pattern recognition, quantitative and neural network models. In this paper, a computer algorithm of new quantitative models for flank and crater wear estimation is presented. First, a quantitative model based on a correlation between increases in feed and radial forces and the average width of flank wear is developed. Then another model which relates acoustic emission (AErms) in the turning operation with the flank and crater wear developed on the tool is presented. The flank wear estimated by the first model is then employed in the second model to predict the crater wear on the tool insert. The influence of flank and crater wear on AErms generated during the turning operation has also been investigated. Additionally, chip-flow direction and tool–chip rake face interfacing area are also examined. The experimental results indicate that the computer program developed, based on the algorithm mentioned above, has a high accuracy for estimation of tool flank wear

On-Line Tool Wear Estimation in CNC Turning Operations using Fuzzy Neural Network Model

In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time

The Total Energy and the Total Entropy of Force Signals - New Parameters for Monitoring Oblique Turning Operations

In unmanned CNC turning operations, the accuracy of tool wear predictions is very important for accurate tool replacement policies and avoiding unnecessary tool insert changes. This paper introduces two new parameters, namely the total energy and the total entropy of force signals, for tool condition monitoring. The correlation between the new parameters, tool wear and a wide range of cutting conditions is examined. The experimental results show that the energy of force signal can be reliably used to monitor tool flank and crater wear over a wide range of cutting conditions. However, the total entropy of forces does not appear to be sensitive to feed rate, rake angle and tool wear. The experimental results also indicate that crater wear causes an increase in the effective rake angle resulting in lower total energy of forces. For some particular shapes of worn tool, however, the crater wear results in a decreased rake angle which increases the total energy of forces. The influence of crater wear on forces and the root mean square of acoustic emission (AErms) signals is also observed in this research