Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.

Review of tool condition monitoring methods in milling processes

The most important improvement in metal the cutting industry is the continuous utilization of cutting tools and tool condition monitoring system. In the metal cutting process, the tool condition has to be administered either by operators or by online condition monitoring systems to prevent damage to both machine tools and workpiece. Online tool condition monitoring system is highly essential in modern manufacturing industries for the rising requirements of cost reduction and quality improvement. This paper summaries various monitoring methods for tool condition monitoring in the milling process that have been practiced and described in the literature.

Tool condition monitoring techniques in milling process — a review

Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning

An efficient tool condition monitoring system was designed for keyway milling of 7075-T6 hybrid aluminium alloy composite with resultant machining force and sound acquired while the milling process...

Prediction of cutting tool wear during milling process using artificial intelligence techniques

Audio signal analysis for tool wear monitoring in sheet metal stamping

Owing to the inherent complexity and variability of the machining process, the sound signals of the cutting process are usually polluted by chip breakage signals and environmental noise which makes it very difficult for tool breakage detection based on sound signals. An approach based on empirical mode decomposition (EMD) and independent component analysis (ICA) is presented to deal with the blind source separation problem of cutting sound signals in face milling with the objective of separating cutting oriented sound signals from those background noises. The advantage of EMD is its ability to adaptively decompose an arbitrary complicated time series into a set of components, called intrinsic mode functions (IMFs). With EMD, cutting sound signals in face milling process are composed into a set of IMFs. Using fast ICA to analyze these series, some independent components are obtained, from which different types of sound signals can be extracted. Experimental results show that the proposed EMD-ICA method is ...

Cutting sound signal processing for tool breakage detection in face milling based on empirical mode decomposition and independent component analysis

The invention discloses a method for realizing order analysis of rotating machinery, belonging to the technical field of signal processing. The method comprises the following steps of: extracting a vibration signal and a rotation speed signal of equipment by using a vibration acceleration sensor; performing anti-aliasing filtering by using an anti-aliasing filter; generating a digital signal by using a data acquisition card; generating an analog pulse by using a transmission ratio parameter and performing equal-angle resampling on the vibration signal; and realizing order analysis through spectrum conversion. By adopting the method, the defect of the need of additionally arranging a rotating speed signal sensor on an enterprise field for realizing order analysis is overcome. The method plays an important role in improving the technology of the conventional equipment.

Method for realizing order analysis of rotating machinery

Fast Fourier transform-based near-field acoustic holography requires that the acoustic field should be sampled with a uniform spacing which is less than a half wave length of the highest frequency of interest to avoid spatial aliasing. Therefore, a large number of microphones are demanded for high frequencies. To overcome this difficulty and enhance the reconstruction accuracy, a data interpolation method based on the wave superposition algorithm (WSA) is proposed. At first, WSA is employed to regenerate the acoustic pressures between the measurement points. Then, the accuracy of the regenerated data is improved through an iterative process. In the end, taking the interpolated pressures as input data, the acoustic field can be reconstructed precisely with relatively few measurements. To solve the inherent ill-posed problem, Tikhonov regularization method together with generalized cross validation parameter choice method is utilized in the data interpolation method. Numerical simulation and experimental re...

Data interpolation method based on the wave superposition algorithm

Fast Fourier transform-based near-field acoustic holography requires that the measurement aperture should completely enclose the source, which is impractical for large-scale sound sources. Helmholtz equation least-squares method can reconstruct the acoustic field with fewer measurements than fast Fourier transform-based near-field acoustic holography. However, it is not suitable for reconstructing acoustic radiation from multiple sources or a complicated source which consists of several separated parts. To circumvent this difficulty and enhance the reconstruction accuracy, a new data extrapolation method based on the modified Helmholtz equation least-squares method is proposed. The base of this data extrapolation method is a modified Helmholtz equation least-squares method which expresses the acoustic field of a complicated source as the superposition of the acoustic radiation from all of its separated parts. Using the acoustic pressures reconstructed with the modified Helmholtz equation least-squares met...

Ran Wang

Papers

Cutting sound signal processing for tool breakage detection in face milling based on empirical mode decomposition and independent component analysis

Method for realizing order analysis of rotating machinery

Data interpolation method based on the wave superposition algorithm

A new data extrapolation method based on the modified Helmholtz equation least-squares method