Online Monitoring Of Tool Wear In Drilling and Milling By Multi-Sensor Neural Network Fusion

Kandilli, İsmet; Sönmez, Murat; Ertunç, H. Metin; Çakır, Bekir

doi:10.1109/icma.2007.4303752

Cited by 10 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach involves collecting data, generally using multiple sensors to determine the wear level, indicating the most economical moment for changing the tool. Kandilli et al [10] monitored drilling and milling processes in real time using force sensors (x, y and z), acceleration, current, and acoustic emission installed in a 4-axis CNC machining center. The mean, standard deviation and RMS of the collected signals were presented to an MLP ANN to identify wear.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In drilling operation, the artificial neural networks have been employed in monitoring of drill wear with aid of sensors that can acquire many signals. The various types of signals employed include those produced during machining and obtained by dynamometer loads [7], electrical current obtained by the application of Hall-effect sensors on electric motors [8], vibrations [9] and also a combination of these and other sensors such as accelerometers and acoustic emission sensors [10]. The status of the wear is analysed with base on input variables such as cutting speed, feed rate, drill diameter, drill geometry among others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

Aguiar

Silva

Gerônimo

et al. 2016

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

RBF) neural network were trained to estimate the diameter of machined holes. The multisensory approach includes an acoustic emission sensor, accelerometer, dynamometer and an electric power sensor. The optimum configuration for each artificial intelligence system was determined based on algorithms designed to examine the influence of each system's signals and specific parameters on the final result of the estimate. The results indicated the MLP ANN was more robust in withstanding data variation. The ANFIS system and RBF network showed markedly varying results in response to variations in the obtained data during training, suggesting these systems should always be trained with the dataset presented in the same order. A satisfactory response between the multisensory approach and MLP network was observed. The vertical component of force, along the z axis, was the only parameter able to present valid results for all the artificial intelligence systems analysed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

Aguiar

Silva

Gerônimo

et al. 2016

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Many applications employ neural networks. In [6], where multi-sensor data are used as input and the system is trained for monitoring of tool wear in drilling and milling. Also, the authors of [7] present a model for sensory fusion using an Echo State Network to predict object trajectories based on the information from several radars.…”

Section: Introductionmentioning

confidence: 99%

Positioning control on a collaborative robot by sensor fusion with liquid state machines

Sala

Brusamarello

Azambuja

et al. 2017

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

A positioning controller based on Spiking Neural Networks for sensor fusion suitable to run on a neuromorphic computer is presented in this work. The proposed framework uses the paradigm of reservoir computing to control the collaborative robot BAXTER. The system was designed to work in parallel with Liquid State Machines that performs trajectories in 2D closed shapes. In order to keep a felt pen touching a drawing surface, data from sensors of force and distance are fed to the controller. The system was trained using data from a Proportional Integral Derivative controller, merging the data from both sensors. The results show that the LSM can learn the behavior of a PID controller on different situations.

show abstract

“…For the research corresponding to the sensor fusion techniques, Kandilli et al [20] integrated the current, sound, vibration, and cutting force to identify the tool condition in drilling and milling. Their approach combines vibration signals with other types of sensors used in tool condition monitoring and applies different classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Hsieh

Chiou

2011

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor

show abstract

Online Monitoring Of Tool Wear In Drilling and Milling By Multi-Sensor Neural Network Fusion

Cited by 10 publications

References 21 publications

Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

Positioning control on a collaborative robot by sensor fusion with liquid state machines

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Contact Info

Product

Resources

About