Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Li, Xiaoli; Tso, S.K.; Wang, Jun

doi:10.1109/5326.885116

Cited by 92 publications

(3 citation statements)

References 16 publications

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“…Where x is input, y is output and ‫ܣ‬ ሚ and ‫ܤ‬ ෨ are fuzzy sets which defined on ܴ ෨ . This type of fuzzy inference, due to its simple structure, is more applicable in software prepared for industrial applications [12]. The on-line measured current I is compared with estimated currents S i by a fuzzy classification method.…”

Section: Detection Of Tool Wear By Using Fuzzy Classificationmentioning

confidence: 99%

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

Tanabi

Babaei

2011

AMR

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On-line tool wear condition monitoring is a significant area of research in automated machining process. This study concerns the development of an on-line tool wear condition monitoring system based on a fuzzy logic scheme. The feed motor current was measured and used as the input of a fuzzy process. Previously, most studies in this field have assumed the workpiece as rigid. However, in practice, cutting force causes the elastic deflection on workpiece and changes actual cutting depth. Therefore, due to coupling between actual depth of cut and feed motor current, the measured current values vary on cutting length according to workpiece elastic deflection pattern. This oscillation in current signal which is used as the input of monitoring system, can affect output results. Thus, in this study in order to achieve more accurate results, the workpiece clamping condition has been considered and through monitoring software a correction factor has been applied on outputs. Experimental results indicate that the proposed method can be reliable for practical applications of tool monitoring systems.

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Section: Detection Of Tool Wear By Using Fuzzy Classificationmentioning

confidence: 99%

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

Tanabi

Babaei

2011

AMR

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“…For the implementation of monitoring schemes, most of the current studies on TCM are based on empirical analysis [3], or sensing-oriented approaches such as cutting forces [4]- [8], motor current analysis [9], [10], vibration analysis [11], [12], and acoustic emission (AE) [13], [14]. Several comprehensive surveys of these works have been published [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

et al. 2019

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Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. A robust and efficient tool condition monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality, and benefit the cost control of manufacturers. This paper presents a systematic singularity analysis approach of cutting force and vibrations for feature extraction of TCM in milling. The singularity of sensory signals is estimated by Holder Exponents (HE), which are determined by wavelet transform modulus maxima (WTMM). A comprehensive wavelet basis selection approach is proposed to choose the appropriate wavelet basis for different sensory signals. A de-noising algorithm based on WTMMs' estimation was used as a pre-processing technique to improve noise reduction and preserve the singularities. The mutual information method was employed to rank HE features. The effectiveness of the singularity analysis approach is validated through the Support Vector Machine (SVM) models trained by these ranked features. The estimating results of case studies confirm the efficacy and efficiency of the proposed approach.

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“…For the monitoring purposes in machining, the majority of the current researches on TCM are employing the indirect sensor-driven monitoring means. The commonly-used sensing signals include cutting force [3]- [7], vibration [8], [9], acoustic emission [10], [11], sound [12], temperature [13], spindle current or power [14], [15], etc. The correlation model between the sensor signals and tool conditions would be established to implement the TCM tasks.…”

mentioning

confidence: 99%

The Monitoring of Milling Tool Tipping by Estimating Holder Exponents of Vibration

et al. 2020

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The gradual tool wear is unavoidable in the machining process, it directly influences the surface integrity and dimensional tolerances of the components. The tool condition monitoring (TCM) systems have the capacity to make full use of the cutting potential of the cutting tools, which is of great significance for improving production efficiency and ensuring product quality. In milling titanium alloys, tool tipping is one of the main failure modes. Therefore, this study focuses on establishing a tool tipping monitoring approach for the milling process. The singularity analysis method based on wavelet transform was employed to characterize the variation of vibration waveforms quantitatively with the Holder Exponent (HE) index. The probability density distribution and statistical analysis were adopted to extract effective HE features from the HE indexes to correlate with the different tool conditions. The mutual information method was adopted to rank the discriminability of the HE statistical parameters. Then, several machine learning (ML) models were established with the screened HE features. Finally, the results of the experiments indicate that the Support Vector Machine (SVM) model has the highest classification accuracy and can provide practical guides on the tool changes.INDEX TERMS Tool tipping, titanium alloys, vibrations, Holder Exponents, support vector machine. I. INTRODUCTION

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Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Cited by 92 publications

References 16 publications

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

The Monitoring of Milling Tool Tipping by Estimating Holder Exponents of Vibration

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