Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. (August 2021)

• Record Type: Journal Article
• Authors: Zhou, Chang'an; Guo, Kai; Sun, Jie
• Is Part Of: Mechanical systems and signal processing. Volume 157(2021)
• Publication Date: 2021-08
• Subjects: Sound -- Tool condition monitoring -- Singularity -- Holder exponents -- Mutual information; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107738
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Singularity analysis of sound signals was correlated to tool wear progression in milling. The proposed denoising algorithm can improve the SNR while preserving singularity. HE features are correlated to tool conditions and independent of most cutting parameters. Abstract: Manufacturing plays an important role since they are among the largest energy consumers in modern societies. With the enhancement of environmental protection and a severe shortage of energy and resources globally, sustainable manufacturing technology has been recognized as an important future trend of manufacturing industries. Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. Therefore, 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. Even though lots of tool condition monitoring systems have been established using various sensors, there is still an urgent demand for a low-cost and simple setup system. This article presents a sound singularity analysis approach for TCM in milling; this approach has never been previously employed for milling tools. A de-noising algorithm based on Wavelet Transform Modulus Maxima (WTMM) estimation was proposed to eliminate noises and preserve singularities. Then a wavelet basis selection method was established for optimal sound singularity analysis. The singularity was estimated by Holder Exponents (HE). A full tool life-cycle milling experiments were conducted to obtain the sound signals. The mutual information method was employed to rank HE features. The Means, Standard deviations, Minima of estimated HEs and Quantities of singular points are found most correlated to tool conditions. Then a Support Vector Machine (SVM) model trained by these features for TCM has been proposed, achieving classification accuracy of 85%. Finally, the manufacturing sustainability of the TCM approach was evaluated by tracking and improving the usages of ten same type cutters in the CNC manufacturing plant. Experimental results indicate that this approach is efficient and capable of providing effective guidance on tool replacement, and can enhance the manufacturing sustainability.