Letter to the Editor

Chatter recognition by a statistical evaluation of the synchronously sampled audio signal

Robot milling chatter monitoring is susceptible to interference from robot pose, feed direction, inertial excitation-dominated low-frequency flexible vibration (IELFFV), and spindle noise. Therefore, this paper proposes a multi-type chatter detection method utilizing multi-channel internal and external signals. First, the multichannel vibration signals and internal controller signals were acquired and aligned. The vibration signals were converted into an engagement coordinate system. Then, based on the milling vibration signal characterization and sensitivity analysis of vibration signals to chatter, a combination of three chatter indicators, calculated via filtered displacement, filtered acceleration, and internal signals, was utilized to detect structural mode-dominated low-frequency chatter, IELFFV, and tool mode-dominated high-frequency chatter. Finally, the effectiveness of the proposed method in detecting the multitype chatter was demonstrated via robot milling experiments. However, the process of the tool entering and exiting the workpiece could be misclassification as TMHFC, which would be addressed in subsequent research.

Machine tool vibrations impose severe limitations on industry. Recent progress in solving for the stability behavior of delay differential equations and in modeling milling operations with time delay differential equations has provided the potential to significantly reduce the aforementioned limitations. However, industry has yet to widely adopt the current academic knowledge due to the cost barriers in implementing this knowledge. Some of these cost prohibitive tasks include time-consuming experimental cutting tests used to calibrate model force parameters and experimental modal tests for every combination of tool, tool holder, tool length, spindle, and machine. This paper introduces an alternative approach whereby the vibration behavior of a milling tool during cutting is used to obtain the necessary model parameters for the common delay differential equation models of milling.

The unstable statuses in machining, such as chatter and severe forced vibration, limit manufacturing productivity and quality. Although there have been plentiful studies on the on-line sensing signals and off-line surface images to identify these statuses qualitatively, the quantification of chatter severity, which is of great significance on machining stability, has been rarely reported. Workpiece surface morphology is the direct result and thus the objective criteria of unstable machining. This paper proposes a new algorithm to quantify the chatter status based on the milled surface image analysis of end-milling. Besides, the proposed method can also identify the stable, forced vibration, and chatter statuses. Specifically, this proposed algorithm extracts two indexes, called Index_Dmean and Index_Emean, via processing the milled surface image. The definitions and calculations of these two indexes are given in Section 2. Index_Dmean is used to differentiate stable and unstable surfaces and quantify the severity of chatter status, and Index_Emean can distinguish between forced vibration and chatter surfaces. Experimental results demonstrate the availability of the proposed algorithm on the chatter quantification and milling instability classification performance. In addition, an applicable on-line vision system with satisfying performance based on the algorithm is developed.

As one of the most unfavorable factors, chatter will seriously hinder the improvement of production efficiency. The weak chatter identification is an effective method for stable cutting. In this paper, a weak chatter identification method is developed using sparse dictionary on account of the milling dynamic responses. As the bases for chatter identification, chatter frequencies are calculated based on the milling dynamic equations. In order to decrease false alarm rate due to the measured background noise, the noise estimation based filtering is proposed. Besides, in consideration of the parameter uncertainty, an adaptive critical bandwidth analysis is implemented with the developed energy proportion index in case of missed alarm. Then, the sparse dictionary matrix is constructed based on the obtained chatter frequencies and critical bandwidth. Finally, the gradient projection for sparse reconstruction (GPSR) algorithm is adopted for chatter frequencies extraction. The proposed method can decrease the measured background noise, obtain critical chatter band and extract weak chatter frequencies accurately. Experimental results shows that the developed method can be successful in weak chatter identification.

In order to be used in the CNC machining center, one rotary ultrasonic machining (RUM) system needs to adapt to various cutting tools. Corresponding ultrasonic vibration units are often designed to work in a local resonant state rather than a full resonant state due to the diversity and complexity of tool shapes. Since the frequency at which the vibration of various cutting tools reaches the maximum is probably different, it is necessary and important to in-situ determine the working frequency of cutting tools in RUM. The key to determining working frequency is to characterize the tool vibration. Although the existing optical and electrical methods have been widely used for tool vibration characterization in some scenarios, they still have obvious limitations when applied to in-situ characterization in RUM systems. In this context, an in-situ characterization method for the tool ultrasonic vibration based on radiated acoustic pressure is presented in the paper. Firstly, characteristics of acoustic pressure distributions radiated from typical cutting tools in RUM are comprehensively studied based on analytical analysis, numerical simulation, and experimental measurement. Secondly, based on the relationship between the acoustic pressure and tool vibration, the measurement strategy and corresponding in-situ measurement system for acoustic pressure are developed to characterize the tool vibration in RUM. Finally, a series of experiments are conducted by using the measurement system, including determining the working frequency of cutting tools and quantitatively describing the vibration amplitude in RUM. It is shown that the proposed method is efficient and cost-effective for the in-situ characterization of the ultrasonic vibration of various cutting tools in RUM, especially for complexly shaped tools working in a local resonant state.

This paper proposes an incipient chatter detection method to meet high dynamic applications’ time and reliability constraints, such as high-speed milling involving heavy noise. The herein introduced method relies on a multiple sampling per revolution (MSPR) technique, coupled with two data preprocessing techniques, a modified adaptive cumulative chatter indicator, and a two-risk levels-based threshold. The MSPR technique enables collecting information-rich enough data to characterize the chatter dynamics thanks to a significant amount of data collected in each revolution. Therefore, the MSPR technique allows for acquiring the data using a short-time window, thus reducing the detection delay. Two data preprocessing techniques, i.e., Z-score normalization and mean-centered, are implemented for data integration and chatter information consolidation. The modified adaptive cumulative chatter indicator has three advantages: (a) it accumulates the information on the chatter feature and highlights the appearance of an incipient chatter; (b) it adapts to the variation of the environmental disturbance noises, resulting in enhanced detection reliability; (c) it is faster than the adaptive cumulative log-likelihood ratio (ACLLR) for decision-making statistically. The two-risk levels-based threshold overcomes the limitations of a unique threshold, and allows simultaneously assessing the two risk levels, thus improving detection reliability. We successfully applied the proposed method to detect incipient chatter in a digital high-speed milling process and assessed its effectiveness by comparing it with several existing chatter detection methods.