Tool Condition Monitoring in Turning Using Statistical Parameters of Vibration Signal

In order to accurately estimate tool life for milling operation, a novel tool condition monitoring system was proposed to improve classifying precision in different cutting condition. Lots of features were extracted from cutting forces signal, vibration signal and acoustic emission signal by different signal processing method, only a few features selected by principal component analysis (PCA) according to contribution rate, and constructed as input vector. The relation between tool condition and features was built by radial basis probability neural network which control parameter of kernel function and hidden central vector were optimized by improved genetic algorithm. The experimental results show that the method proposed in the paper achieves higher recognition rate, good generalization ability and better available practicality.

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A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Sensors ◽

10.3390/s18113866 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3866 ◽

Cited By ~ 16

Author(s):

Yuqing Zhou ◽

Wei Xue

Keyword(s):

Feature Selection ◽

Condition Monitoring ◽

Optimal Parameter ◽

Tool Condition Monitoring ◽

Numerical Control ◽

Selection Methods ◽

Statistical Parameters ◽

Time Frequency ◽

Feature Parameters ◽

Tool Condition

Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

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Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Sensors ◽

10.3390/s20030885 ◽

2020 ◽

Vol 20 (3) ◽

pp. 885 ◽

Cited By ~ 5

Author(s):

Jitesh Ranjan ◽

Karali Patra ◽

Tibor Szalay ◽

Mozammel Mia ◽

Munish Kumar Gupta ◽

...

Keyword(s):

Condition Monitoring ◽

Fuzzy Inference ◽

Wavelet Packet ◽

Vibration Signal ◽

Tool Condition Monitoring ◽

Hole Quality ◽

Inference System ◽

Tool Condition ◽

Micro Drilling ◽

Sensor Signals

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

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