Sage Journals: Discover world-class research

Abstract

Maintaining exceptional product quality and boosting processing efficiency requires precise evaluation of various aspects of the turning process, including the cutting depth, feed rate, and size of the workpiece. This article presents a novel approach for observing the turning process state using modulation signal bispectrum (MSB) and motor current signals. A nonlinear model was established that clarifies the load torque oscillations during turning, which in turn affects the amplitude and phase modulation of the motor stator current. Random noise can be efficiently minimized using the MSB algorithm, allowing the extraction of the current-modulation characteristic sideband phase and amplitude from the collected current signal. This technique enables clear representation and enhanced monitoring of load torque changes throughout the turning process. The proposed method was validated via mathematical simulations and universal lathe tests, with the results indicating that the MSB phase and amplitude values effectively capture both dynamic and static torque alterations during the turning operation, making this approach a valuable tool for overseeing the turning process.

Keywords

Current modulation modulation signal bispectrum motor current signature analysis torque oscillation turning processing

Introduction

The domains of intelligent machining systems have a vested interest in machine-tool monitoring methodologies and proactive fault detection. One crucial machining operation is turning, a process that meticulously removes superfluous material from revolving cylindrical workpieces. Monitoring this operation is fundamental to enhancing machining quality, safeguarding expensive equipment, and guaranteeing the efficient functioning of systems. Factors such as the workpiece material, cutting tool wear, and operating environment introduce variable elements into the mechanical machining process, inducing changes over time.¹ These alterations lead to probabilistic and unpredictable scenarios during the turning process, complicating precise prediction and control. Therefore, the necessity for ultra-precise monitoring systems cannot be overstated. These systems are designed to track the myriad changes occurring throughout the turning process meticulously, ensuring precision, quality, and optimal control in machining.

At present, two primary types of approaches exist for monitoring machining conditions: direct and indirect methods.¹ In laboratories, direct measurements are generally conducted using optical sensors,² force sensors,^3,4 and other similar techniques to assess tool wear and workpiece surface roughness; one effective method, for instance, involves utilizing a machine vision system to evaluate tool conditions.^5,6 However, this approach necessitates interrupting the processing for image acquisition, limiting its applicability for online monitoring.⁷ In contrast, the indirect method involves monitoring the state of the processing system by analyzing the interrelationships among various parameters, such as acoustic emission,^8,9 vibration,¹⁰ and sound.¹¹ Vibration sensors, commonly installed on the workpiece, are widely used in this context, but their signal amplitude varies over time during processing, adding to the complexity of the analysis.^12,13 Numerous studies have demonstrated that combining multiple sensors enhances monitoring reliability and system robustness; however, these monitoring methods rely on external sensors.^14–16

Changes in the processing conditions of a mechanical device, such as variations in the material or shape of the workpiece or the condition of the cutting tool, alter both static and dynamic loads on the mechanical system. Changes in torque applied to the workpiece are transmitted through the mechanical transmission system to the motor shaft; this electromechanical coupling causes fluctuations in the magnetic flux of the motor, which, in turn, induces modulation of the motor current.¹⁷ Consequently, motor current signature analysis (MCSA) has been proposed as a method of monitoring machining processes.¹⁸

Current signals are advantageous in monitoring machining operations due to their immunity to time-varying transmission path effects, ease of acquisition,¹⁹ and minimal interference with the processing itself, enabling cost-effective monitoring.²⁰ Extensive research has been conducted to explore the relationship between current and cut force.^20–23 Kuntoğlu et al. ⁶ employed seven different sensor types to monitor the cutting area and identified the current sensor as the third most effective in evaluating tool wear. However, utilizing the current sensor for monitoring presents challenges due to the introduction of transmission chain noise, resulting in a low signal-to-noise ratio and signal processing complexities. Consequently, the current sensor has been sparingly used (8.1%) for machining monitoring. Typically, it is combined with other sensor signals to achieve optimal performance. Nevertheless, it is suggested that the current signal, containing information about the transmission chain, can facilitate non-invasive and cost-effective global monitoring of the overall mechanical system.

To fulfill the aforementioned objectives, signal processing techniques need improvement to extract subtle signal variations and determine system state. Commonly employed methods for signal processing, including the wavelet transform, empirical mode decomposition (EMD), morphological signal processing (MSP), and spectral analysis,^23–25 have been used for monitoring various aspects of machining processes, such as tool wear,²⁶ machine conditions,²⁴ and chatter.^27,28 However, they suffer from limitations such as the limited length of basis functions in wavelet analysis, mode confusion in EMD,²⁹ and noise interference in MSP, weakening the correlation between state features and current signals. Furthermore, these methods primarily focus on amplitude diagnosis and overlook the inherent modulation characteristics of the current signal. Therefore, the nonlinear amplitude and phase interactions of the current must be described to identify small frequency variations around the power supply frequency effectively. Consequently, even slight changes in cutting parameters can impact cutting tools, workpiece size, and other relevant factors.

Bispectrum analysis, a widely employed technique for high-order spectrum analysis, utilizes third-order statistics to examine spectral characteristics and is particularly useful for detecting nonlinear and non-Gaussian systems and preserving phase information. A more precise and effective alternative to the power spectrum in analyzing modulated signals is the modulation signal bispectrum (MSB).³⁰ Gu et al. ³¹ demonstrated that the MSB enhances the complex modulation components of rotating machines, enabling more accurate diagnosis of broken rotor bars in induction motors (IMs) by detecting and quantifying sidebands in current signals, effectively mitigating random noise and interference through phase invariance and signal alignment.³² Previous research has shown that MSB analysis is capable of early fault detection and diagnosis in various rotating machines, including reciprocating compressors,³³ rolling bearings,³⁴ and gearboxes.³⁵ However, its application in monitoring machining conditions and fault diagnosis has not been explored.

This study focuses on utilizing the amplitude and phase of the MSB to enable machining-condition monitoring, with turning experiments conducted under different machining conditions to measure instantaneous currents and extract accurate eigenvalues of states. The remainder of this paper is structured as follows. Section II introduces the proposed theoretical method. Section III presents the conclusions drawn from numerical simulations. Section IV discusses the experimental setup used to validate the proposed method. Section V presents the main findings of the study. Finally, Section VI concludes the paper.

Methodology

Turning forces

The spindle-motor drive system, a crucial mechanical component in a lathe, comprises several essential elements, including pulleys, belts, gearboxes, and spindles, all working together to ensure smooth operation. Figure 1 provides a schematic representation of the primary drive chain employed in a lathe. Variations in the process parameters, tool wear, and overall health of the machine tool can induce torque oscillations in IM, further emphasizing the importance of maintaining optimal conditions.

Figure 1.

Universal lathe setup.

During lathe machining operations, the cutting forces play a significant role in determining the resistance encountered, as illustrated in Figure 2. The turning force ( $F$ ) can be decomposed into three distinct components: primary cutting force ( $F_{c}$ ), feed force ( $F_{f}$ ), and radial force ( $F_{r}$ )³⁶:

F = \sqrt{F_{f}^{2} + F_{r}^{2} + F_{c}^{2}} .

(1)

Figure 2.

Schematic of the cutting force.

The main cutting torque is generated by $F_{c}$ , and the empirical expression for $F_{c}$ is³⁷

F_{c} = C_{F} a_{p}^{X_{F}} f^{Y_{F}} v_{c}^{n_{F}} K_{F},

(2)

where $v_{c}, f$ , and $a_{p}$ represent the cutting parameters that respectively represent the cutting speed, feed rate, and depth of cut (DOC); these parameters must be specified before processing. $C_{F}, X_{F}, Y_{F}, n_{F},$ and $K_{F}$ are correction coefficients dependent on the cutting conditions and workpiece material.

The cutting force can be decomposed into two distinct components – static and dynamic forces, which can be mathematically formulated as

F = F_{0} + Δ F .

(3)

The static component of the cutting force ( $F_{0}$ ) is primarily influenced by machining parameters and tool wear, while system vibrations and irregular tool wear affect the dynamic component $Δ F$ . During the cutting process, dynamic forces cause deviations from the original motion track, as illustrated in Figure 3, making the cut unreliable.

Figure 3.

Schematic of the dynamic cutting force.

Influence of mechanical load oscillations on the current signal

The dynamic cutting force is well-known for its ability to induce fluctuations in the load torque of the IM, consequently causing variations in the load and generating oscillations in the airgap eccentricity of the IM. Such torque oscillations, arising from imbalances in the load, are typically characterized by specific frequencies often correlated with the speed of the mechanical motor. The load torque ( $T_{L}$ ) experienced over time (t) can be mathematically expressed as follows³⁰:

T_{L} (t) = (F_{0} + Δ F) r = T_{0} + T_{k} \cos (2 π f_{k} t + α_{k}),

(4)

where $r$ is the radius of the processed workpiece and $α_{k}$ is the initial phase. The variation of load torque ( $T_{L})$ over time can be modeled using a constant component ( $T_{0}$ ) and an additional component that oscillates at the rotation frequency ( $f_{k}$ ). The first term in the variable-component Fourier series corresponds to the cosine of frequency ( $f_{k}$ ); this study focuses on fundamental terms and excludes higher-order terms to enhance clarity. $T_{k}$ , the mechanical torque of the spindle motor, can be expressed as follows³⁸:

{\begin{matrix} T_{e} = T_{L} + \frac{J}{p_{n}} \frac{d ω_{r}}{dt} \\ u = Ri + L \frac{di}{dt} + \frac{dL}{d α_{r}} ω_{r} i \\ ω_{r} = \frac{d α_{r}}{dt} \end{matrix} .

(5)

In equilibrium, the motor torque ( $T_{e})$ is equivalent to the constant component ( $T_{b}$ ) of the load torque. The mechanical rotor position is denoted by $α_{r}$ , whereas $ω_{r}$ represents the angular velocity of the rotor. The number $p_{n}$ and total inertia of the rotational components of the motor are symbolized by $J$ . The stator current and supply voltage are indicated by $i$ and $u$ , respectively, whereas the motor inductance is represented by $L$ . The equations of motion achieve dynamic balance, which is manifested in the motor speed ( $ω_{r})$ as expressed by³⁹

\begin{matrix} ω_{r} (t) = - \frac{1}{J} \int_{t_{0}}^{t} T_{k} \cos (2 π f_{k} τ + α_{k}) d τ + C \\ = - \frac{T_{k}}{2 J π f_{k}} \sin (2 π f_{k} t + α_{k}) + ω_{r 0} \end{matrix},

(6)

where $C$ denotes the integration constant. The velocity fluctuation resulting from the undulating torque at ( $f_{k}$ ) comprises a constant component ( $ω_{r 0}$ ) and a sinusoidal component, demonstrating that torque oscillation leads to velocity fluctuation.

The mechanical rotor position, represented by $α_{r}$ , is derived from the integral of the mechanical speed:

α_{r} (t) = \int_{t_{0}}^{t} ω_{r} (τ) d τ = \frac{T_{k}}{J . 4 π^{2} f_{k}^{2}} \cos (2 π f_{k} t + α_{k}) + ω_{r 0} .

(7)

Consequently, the angular variation induces phase modulation (PM) of the magnetic flux. The magnetic flux $Φ (t)$ of any phase can be generally described as the sum of stator and rotor fluxes¹³:

\begin{matrix} Φ (t) = Φ_{s} \cos (2 π f_{s} t + φ_{s}) + \\ Φ_{r} \cos (2 π f_{s} t + φ_{r} + β \cos (2 π f_{k} t + α_{k})) \end{matrix},

(8)

where $β = \frac{p T_{k}}{4 π^{2} {Jf}_{k}^{2}}$ is the modulation index and $φ_{s}$ is the initial phase. $Φ_{s}$ denotes the stator flux amplitude, and $Φ_{r}$ is the rotor flux amplitude. A notable feature of this PM is the introduction of $β \cos (2 π f_{k} t + α_{k})$ term in the flux wave phase. Given physically reasonable values of $J$ , $T_{k}$ , and $f_{s}$ , the approximation $β << 1$ generally holds.

The relationship between stator current $i (t)$ and magnetic flux $Φ (t)$ is provided by the stator voltage equation³⁸:

u (t) = R_{s} i (t) + \frac{d Φ (t)}{dt} .

(9)

Given the imposition of $u (t)$ by the voltage source, the resultant stator current is proportional to the time derivative of the phase flux $Φ (t)$ , exhibiting an equivalent frequency content. The stator current of a random phase can be obtained by integrating equations (8) and (9), expressed in a comprehensive form as^39,40

\begin{array}{l} I (t) = i_{s t} (t) + i_{r t} (t) = I_{s t} \sin (2 π f_{s} t + φ_{s}) \\ + I_{r t} [1 + \frac{f_{k}}{f_{e}} β \cos (2 π f_{k} t + α_{k})] \\ \times \sin (2 π f_{s} t + φ_{r} + β \cos (2 π f_{k} t + α_{k})) . \end{array}

(10)

The current components of the stator and rotor, denoted by $i_{st} (t)$ and $i_{rt} (t)$ , respectively, are induced by their respective magnetic fields, characterized by amplitudes $I_{st}$ and $I_{rt}$ , respectively. $φ_{s}$ and $φ_{r}$ represent the initial phases of the magnetic fields of the stator and rotor, respectively. Equation (10) shows that the rotor current experiences amplitude modulation (AM) and PM.

When $β \cos (2 π f_{k} t)$ is extremely small, cos( $β \cos (2 π f_{k} t + α_{k})$ ) ≈1, and sin( $β \cos (2 π f_{k} t + α_{k})$ ) ≈ $β \cos (2 π f_{k} t + α_{k})$ . Consequently, equation (7) can be reduced to the superposition of three distinct components:

\begin{array}{l} I (t) = I_{s t} \sin (2 π f_{s} t + φ_{s}) + \\ I_{r t} β c o s (2 π f_{k} t + α_{k}) s i n (2 π f_{s} t + φ_{r}) \\ = I_{s t} s i n (2 π f_{s} t + φ_{s}) + \\ \frac{I_{r t} β}{2} s i n [2 π (f_{s} - f_{k}) t + φ_{r} - α_{k}] \\ + \frac{I_{r t} β}{2} \sin [2 π (f_{s} + f_{k}) t + φ_{r} + α_{k}] . \end{array}

(11)

Equation (11) demonstrates that during machining operations under varying mechanical working conditions, the dynamic component causes variations in both torque and speed of the IM, leading to the appearance of additional frequencies in the current spectrum. Relevant frequency sidebands $f_{s} \pm n f_{k}$ are utilized to gather machining information, where $f_{s}$ denotes the supply power frequency, $f_{k}$ denotes the rotational frequency, and n = 1, 2, 3…

Detection algorithm

The analysis presented in Section II B shows that the dynamic component contributes to the AM and PM of the stator current. Whereas AM results in only one pair of edge frequencies, PM leads to an “infinite” number of edge frequencies. The outcome of frequency modulation is an increase in side frequencies due to the phase difference between the PM and AM side frequency components. The coexistence of the two creates an asymmetric distribution of side frequencies.

Conventional bispectrum analysis does not adequately characterize the modulation of motor current signals. Therefore, MSB was employed in this study, calculated using the following expression⁴¹:

B_{MS} (f_{k}, f_{s}) = EX (f_{s} + f_{k}) X (f_{s} - f_{k}) X^{*} (f_{s}) X^{*} (f_{s}) .

(12)

Equation (12) combines $(f_{s} + f_{k})$ and $(f_{s} - f_{k})$ of the modulated amplitude signal into a single metric, reducing redundant information and making it simpler and more intuitive to analyze and identify the source of the signal.

Equation (12) can be formulated in terms of magnitude and phase in the following manner:

B_{MS} (f_{k}, f_{s}) = E [V_{k} e^{j ϕ_{MS} (f_{k}, f_{s})}] .

(13)

The MSB magnitude can then be expressed as

A_{k} = \sqrt{V_{k}} = \sqrt{| B_{MS} (f_{k}, f_{s}) |} .

(14)

Simultaneously considering the variations in amplitude and phase difference of the two sidebands through MSB analysis can significantly improve the accuracy and timeliness of tool-condition monitoring and diagnosis.

The MSB phase can be mathematically expressed as

ϕ_{MS} (f_{s}, f_{k}) = ϕ (f_{s} + f_{k}) + ϕ (f_{s} - f_{k}) - ϕ (f_{s}) - ϕ (f_{s}) .

(15)

The MSB phase is independent of the fundamental frequency phase, enabling the MSB to estimate the phase without considering the fundamental frequency phase. It provides valuable information about the angular position of the modulating signal and operating parameters, independent of the starting point of the rotor angular displacement and signal acquisition. Therefore, using the magnitude and phase of MSB is a feasible means of characterizing machining conditions.

Numerical analysis of sideband responses

Due to the highly complex dynamics of motor drives, obtaining a closed analytical solution to demonstrate frequency transfer responses is challenging. Numerical simulations were conducted using a three-phase IM model, as presented in Table 1, to validate the applicability of the MSB to motor current signals in machining. The simulations were performed by solving equation (5) using the ode45 solver in MATLAB (version: R2021a).

Table 1.

Simulation parameters.

Category	Main parameters	Parameter values
IM	Supply power	3.0 kW
	Supply voltage (V)	220 V
	Supply frequency (f_s)	50 Hz
	Rated torque ( $T_{b}$ )	28.64 N/m
	Mechanical inertia (J)	0.18
	Angular speed ( $ω$ )	105 rad/s
	Rated speed	970 rpm
	Pole pairs (p)	3
	Phase	3
Resistance	Stator resistance $(R_{S}$ )	1.9 $Ω$
Resistance	Rotor resistance $(R_{r}$ )	1.8550 $Ω$
Inductance	Magnetizing inductance per unit length ( $L_{ms}$ )	1.26×10^–6 H/m
	Stator leakage inductance ( $L_{Ls}$ )	0.0072 H
	Rotor leakage inductance ( $L_{Lr})$	0.0072 H
Others	Sampling frequency	4000 Hz
Others	Sampling time	12 s

The combination of MCSA with MSB analysis was implemented under various loading conditions (10%, 50%, and 90% of T_b) to reflect different degrees of tool wear. The values 10%, 50%, and 90% of T_b correspond to light, medium, and heavy load conditions, respectively. These designations were established to facilitate comprehensive evaluation of the system. Additionally, the simulations accounted for inherent motor asymmetries. The presence of airgap eccentricity could be attributed to various factors, including manufacturing errors, unbalanced loads, bearing wear, and bent rotor shafts.

Sideband responses for static loads

First, an investigation was conducted on the variation in the static load of the motor under a dynamic force of 0.5 nm, with the analysis of MSB phase results presented in Figure 4(a). MSB phase values exhibited a proportional increase with the excitation frequency, indicating significant correlations at different frequencies. However, under different static loads (10%, 50%, and 90% of T_b), the phase trends exhibited notable shifts toward lower values. As seen in Figure 4(b), the MSB magnitudes display a significant inverse correlation with increasing frequency. Nevertheless, the values obtained under these different conditions do not exhibit substantial differences. The MSB phase demonstrates greater sensitivity to changes in the static force.

Figure 4.

MSB phase and magnitude under different static loads: (a) phase characteristics and (b) magnitude characteristics.

Sideband responses for dynamic loads

Next, the motor was set to operate at 50% of T_b ( $T_{e}$ =14.32 N), and the dynamic force was gradually increased (0.5, 1.0, and 1.5 nm) in the model to simulate and analyze the response of MSB to changes in dynamic motor force. As depicted in Figure 5(a), the phase value increases proportionally with respect to the rotation frequency, whereas the variation in the dynamic force has a minimal impact on the phase results. Figure 5(b) illustrates a negative correlation between magnitude and frequencies, consistent with the simulation results obtained under a static load. Unlike the static simulation results, the MSB magnitude results accurately depict changes in dynamic forces, yielding more pronounced recognition outcomes for low frequencies.

Figure 5.

MSB phase and magnitude under different dynamic loads: (a) phase characteristics and (b) magnitude characteristics.

During the turning process, fluctuations in machining parameters, tool wear, and spindle vibrations cause oscillations in the motor load. The aforementioned simulation results establish that the MSB magnitude and phase can effectively identify static and dynamic load variations in the motor, making them reliable indicators of turning conditions.

Experimental validation

Experiments were conducted utilizing a universal lathe (CZ6132A) for shaft turning operations. To validate the analytical results and evaluate the efficacy of the proposed system for monitoring the turning process. The drive system of the lathe was powered by a three-phase IM, with all relevant parameters enumerated in Table 2. As illustrated in Figure 6, the IM propels the spindle via the belt pulley. Upon engagement of the clutch, the spindle box is actuated by the primary gear Z1/Z2, which serves dual functions of deceleration and power transmission, thereby enabling spindle rotation. Considering the working characteristics of mechanical machining and the structural composition of the cutting tool, it becomes apparent that the operational state of the driven components exerts a direct impact on the functioning status of the spindle-motor rotor assembly.

Table 2.

IM specifications.

Parameters	Value	Parameters	Value
Power frequency	50 Hz	Voltage	220 V
Power	3 kW	Rated current	7.7 A
Rated speed	970 r/min	Rejoining	Y
Number of phases	3	Rated voltage	380 V
number of p	6	cos(phi)	0.82

Figure 6.

Schematic of the universal lathe machining monitoring system.

To measure the motor current, a high-precision clamp meter was affixed to Phase B of the three-phase power supply of the motor. The measurement range of the current sensor extended up to 40 A, boasting an impressive accuracy of 2%, and an applicable frequency band ranging from 5 Hz to 10 kHz. The output signal from the current sensor was connected to a sophisticated data acquisition system, YMC9004, which was configured with a data accuracy of 24 bits and a sampling rate of 100 kHz.

Figure 7 presents the tool model and dimensions of the machined workpiece. The experimental parameters are documented in Table 3, which includes the spindle speed set at 1080 rpm, corresponding to a rotation frequency of 18 Hz. To assess the ability of current signal analysis in discerning different machining parameters, various DOCs were employed. Three DOCs were utilized in the experiment to match the simulation parameters. The circular shaft was subjected to turning using the RP930 general turning tool, resulting in a diameter reduction from 22 mm to 10 mm. Figure 8(a) visually displays two turning workpieces with different diameters (22 mm and 13 mm), showcasing a distinct periodic signal at 50 Hz. The spectral displays of these signals are presented in Figure 8(b), where numerous sidebands are clearly observed around 50 Hz. To identify and differentiate between these systems amid noise effectively, additional signal processing is necessary.

Figure 7.

Experimental cutting tools and workpieces: (a) PR930 and (b) circular shaft.

Table 3.

Experimental parameters.

Workpiece material	Count of cuts	DOC	Feed rate
A3 steel rod	12	0.5 mm	0.05 mm/r
	6	1 mm
	4	1.5 mm

Figure 8.

Waveforms and spectral comparisons: (a) time-domain waveform and (b) frequency-domain waveform.

To validate the proposed method, the data underwent further processing using the algorithm described in Section II C. The Hanning window was employed to mitigate spectrum leakage in the data frames. Interference suppression involved a 40% overlap of frames, with an average of 100 calculations performed. Figure 9 illustrates typical calculation results, demonstrating that the magnitudes of the MSBs are predominantly concentrated in the frequency slice at $f_{s}$ =50 Hz. Figure 10 compares the 50 Hz slices under different working conditions, specifically, DOC values of 0.5 mm and 1.0 mm. The rotation frequencies and harmonics of all moving parts in the transmission system are displayed. Notably, significant magnitudes can be observed at the belt-passing frequency of 2.87 Hz and its harmonics, indicating a loosening issue in the belt-drive system, which is a common characteristic. Belt transmission can induce pronounced modulation, which is manifested in the motor current spectrum by rich sidebands and their harmonics at the belt-passing frequency.⁴²

Figure 9.

MSB analysis results (DOC = 0.5 mm, diameter = 13 mm).

Figure 10.

MSB magnitudes for $ϕ_{0} = 21 mm$ and two typical DOCs.

The remaining frequencies shown in Figure 10 correspond to the rotations of the lathe spindle, motor shaft, and layshaft. Changes in these frequencies serve as indicators for the turning process. The amplitude at DOC = 1.0 mm is higher compared to that at DOC = 0.5 mm. The MSB results encompass ample information to reflect the machining conditions. To achieve a comprehensive monitoring analysis across various DOCs and workpiece diameters, the MSB analysis was employed to extract the magnitudes of MSB at critical frequencies. Figure 11 presents the 50 Hz slices of the MSB for different workpiece diameters (with DOC = 0.5 mm). Given the pivotal role of the spindle in driving the workpiece rotation, its rotation frequency serves as a key indicator of the overall machining state. Consequently, this feature was extracted for detailed comparative analysis.

Figure 11.

MSB magnitude slices for DOC = 0.5 mm.

MSB phase results

Figure 12 presents the monitoring outcomes acquired from the MSB phases. As anticipated, the MSB phase values exhibited a increase in response to decreasing DOC and workpiece diameter, reinforcing the simulation outcomes discussed in Section III A. As per Section II C, the MSB phase values remain unaffected by the fundamental frequencies, indicating that during machining, the phase values are associated with the static component of the turning process.

Figure 12.

MSB phases for different DOCs.

The predominant influence on the cutting force trend is the quasi-static component, which mirrors alterations in the cutting force needed by the tool to extract metallic material from the workpiece surface. This metallic material mainly stems from the machining parameters and tool wear. Furthermore, equation (2) illustrates a significant correlation between the quasi-static component and the cutting parameters ( $v_{c}, f$ , and $a_{p}$ ). Experimental validation confirms the accuracy of the MSB phase results in reflecting both the DOC and workpiece diameter. The MSB phase values exhibit high sensitivity to fluctuating turning parameters, making them suitable for ensuring surface and dimensional precision during manufacturing.

MSB magnitude results

Due to the direct impact of cutting force/torque on the spindle, the rotational frequency of the spindle was derived through frequency analysis. Figure 13 illustrates the magnitude results at the designated spindle frequency, effectively demonstrating the variation in the DOC. In the case of precision machining with a DOC of 0.5 mm, the magnitude results obtained from the MSB failed to differentiate between changes in machining diameter accurately, consistent with the numerical analysis presented in Section III B, indicating that the magnitude is influenced by both static and dynamic loads. However, its sensitivity to static loads is not significantly high, with its primary feedback mechanism pertaining to dynamic loads.

Figure 13.

MSB magnitudes for different DOCs.

The dynamic cutting force has a negligible impact on the overall cutting force and exhibits a weak correlation with cutting process parameters. The dynamic component is intricate, and irregular wear of the cutting tool introduces dynamic and stochastic elements to the turning force. The system vibration further contributes to these dynamic components. Consequently, the magnitudes associated with the MSB serve not only to monitor changes in cutting parameters during rough machining but also as indicators for identifying abnormal system vibrations or anomalous tool wear.

The MSB phase results are highly sensitive to turning parameter variations and strongly correlate with machining parameters. These phase results mainly detect static changes in load, capturing the quasi-static elements of the turning process, such as machining parameters, tool wear, and cutting force changes. On the other hand, the MSB magnitude results, which are sensitive to turning parameter fluctuations and influenced by both static and dynamic loads, track variations in rough machining parameters and pinpoint unusual vibrations. In essence, the MSB is used to monitor changes in motor load conditions caused by factors such as tool wear, motor asymmetries, and system dynamics changes. It is also instrumental in distinguishing and identifying various machining states and conditions.

Comparison analysis

To analyze static loads, the root-mean-square (RMS) current is typically evaluated. Figure 14 displays the results of the current RMS analysis for different machining parameters. The RMS method effectively identifies changes among different DOCs, but it does not account for variations in the workpiece diameter.

Figure 14.

RMS results for different DOCs.

An envelope spectrum analysis was employed to identify variations in dynamic loads, as depicted in Figure 15. The results showed significant fluctuations near the spindle frequency. The envelope spectrum results indicate system dynamic loading variations. However, significant background noise precludes the use of spindle frequency amplitude from the envelope spectrum as a machining state indicator.

Figure 15.

Envelope results for different DOCs.

The findings demonstrated that the MSB analysis suppresses system interference and reduces high background noise caused by transmission chains. Consequently, it provides reliable indicators for accurately assessing the machining status.

Conclusion

This study comprehensively investigated the effectiveness of MCSA with MSB analysis for detecting different machining states using theoretical analysis, numerical simulations, and experimental evaluations. Our experimental results demonstrated that the phase component of MSB analysis predominantly reflects variations in the quasi-static load, rendering it highly responsive to changes in the machining parameters. This sensitivity enables it to signal variations in the cutting depth and diameter to machinists effectively. In contrast, the magnitude component of MSB analysis, although less sensitive to the machining parameters, relies on both static and dynamic loads. Its sensitivity to dynamic load changes makes it suitable for monitoring system health. In particular, it can detect abnormal wear of turning tools and the presence of abnormal vibrations in the system.

Nonetheless, this study has certain limitations. Although the MSB provides comprehensive insights, refining its resolution could enhance its efficiency. Moreover, variabilities stemming from diverse operating settings or material traits could influence MSB outcomes, signifying the potential for expansive real-time assessments.

Footnotes

Acknowledgements

None.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by Open Fund Project of Key Laboratory of Science and Technology on Integrated Logistics Support under Grant 20210804023 and in part by Guangdong Basic and Applied Basic Research Fund Offshore Wind Power Scheme-General Project under Grant 2022A1515240042.

ORCID iDs

Zhexiang Zou

Chun Li

Fengshou Gu

Data availability

The datasets generated during and/or analyzed during the current study are available in Code Ocean, .

References

Kuntoğlu

Aslan

Pimenov

, et al. A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors (Basel) 2020; 21(1): 108. DOI: 0.3390/s21010108

Sun

Yeh

SS.

Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools. Materials 2018; 11: 1977.

Jemielniak

Urbański

Kossakowska

, et al. Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 2012; 59: 73–81.

Hidayah

Ghani

Nuawi

, et al. A review of utilisation of cutting force analysis in cutting tool condition monitoring. Int J Eng Technol IJET 2015; 15: 150203–154848.

Liu

Zhou

, et al. Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors (Basel) 2019; 19: 3817. DOI: 10.3390/s19183817

Kuntoğlu

Sağlam

Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 2021; 173: 108582. DOI: 10.1016/j.measurement.2020.108582

You

Gao

Guo

, et al. Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation. Mech Syst Signal Process 2022; 171: 108904.

Uekita

Takaya

Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals. Int J Adv Manuf Technol 2017; 89: 65–75.

Kishawy

Hegab

Umer

, et al. Application of acoustic emissions in machining processes: Analysis and critical review. Int J Adv Manuf Technol 2018; 98: 1391–1407.

10.

García Plaza

Núñez López

Beamud González

. Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining. J Manuf Process 2019; 44: 145–157.

11.

Liu

Tseng

Tran

MQ.

Tool wear monitoring and prediction based on sound signal. Int J Adv Manuf Technol 2019; 103: 3361–3373.

12.

Jauregui

Resendiz

Thenozhi

, et al. Frequency and time-frequency analysis of cutting force and vibration signals for tool condition monitoring. IEEE Access 2018; 6: 6400–6410.

13.

Chen

Feng

Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions. Mech Syst Signal Process 2020; 140: 106691.

14.

Khajavi

Nasernia

Rostaghi

Milling tool wear diagnosis by feed motor current signal using an artificial neural network. J Mech Sci Technol 2016; 30: 4869–4875.

15.

Sayyad

Kumar

Bongale

, et al. Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions. IEEE Access 2021; 9: 110255–110286.

16.

Salehi

Albertelli

Goletti

, et al. Indirect model based estimation of cutting force and tool tip vibrational behavior in milling machines by sensor fusion. Procedia CIRP 2015; 33: 239–244.

17.

Goyal

Mongia

Sehgal

Applications of digital signal processing in monitoring machining processes and rotary components: A review. IEEE Sens J 2021; 21: 8780–8804.

18.

Teti

Jemielniak

O’Donnell

, et al. Advanced monitoring of machining operations. CIRP ann 2010; 59: 717–739.

19.

Chen

Feng

Time-frequency space vector modulus analysis of motor current for planetary gearbox fault diagnosis under variable speed conditions. Mech Syst Signal Process 2019; 121: 636–654.

20.

Gritli

Bellini

Rossi

, et al. Condition monitoring of mechanical faults in induction machines from electrical signatures: Review of different techniques. In: Proceedings of the 2017 IEEE 11th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, January 2017, pp.77–84. Doi: 10.1109/DEMPED.2017.8062337.

21.

Shaeboub

Lane

, et al. Modulation signal bispectrum analysis of electric signals for the detection and diagnosis of compound faults in induction motors with sensorless drives. Syst Sci Control Eng 2017; 5: 252–267.

22.

Jeong

Cho

DW.

Estimating cutting force from rotating and stationary feed motor currents on a milling machine. Int J Mach Tools Manuf 2002; 42: 1559–1566.

23.

Yesilli

Khasawneh

Otto

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition. CIRP J Manuf Sci Technol 2020; 28: 118–135.

24.

Kalpana

Mythreyi

Kanthababu

Review on condition monitoring of abrasive water jet machining system. In: International Conference on Robotics, Automation, Control and Embedded Systems2015, pp.1–7. Doi: 10.1109/RACE.2015.7097254.

25.

Yang

Wang

Zan

, et al. Application of bispectrum diagonal slice feature analysis to monitoring CNC tool wear states. Int J Adv Manuf Technol 2022; 120: 5537–5550.

26.

Wong

Chuah

Yap

HJ.

Technical data-driven tool condition monitoring challenges for CNC milling: A review. Int J Adv Manuf Technol 2020; 107: 4837–4857.

27.

Wang

W-K

Wan

Zhang

, et al. Chatter detection methods in the machining processes: A review. J Manuf Process 2022; 77: 240–259.

28.

Urbikain

Olvera

López

Lacalle

, et al. Prediction methods and experimental techniques for chatter avoidance in turning systems: A review. Appl Sci 2019; 9: 4718–18.

29.

Peng

Empirical model decomposition based time-frequency analysis for the effective detection of tool breakage. J Manuf Sci Eng 2006; 128: 154–166.

30.

Shao

, et al. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mech Syst Signal Process 2011; 25: 360–372.

31.

Wang

Alwodai

, et al. A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals. Mech Syst Signal Process 2015; 50-51: 400–413.

32.

Chen

Gear transmission fault diagnosis based on the bispectrum analysis of induction motor current signatures. JME 2012; 48: 84.

33.

Huang

Feng

Tang

, et al. A performance evaluation of two bispectrum analysis methods applied to electrical current signals for monitoring induction motor-driven systems. Energies 2019; 12: 1438.

34.

Tian

Xi Gu

Rehab

, et al. A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram. Mech Syst Signal Process 2018; 100: 167–187.

35.

Guo

Zhen

, et al. Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis. ISA Trans 2020; 101: 408–420.

36.

Kim

DS.

Development of a combined-type tool dynamometer with a piezo-film accelerometer for an ultra-precision lathe. J Mater Process Technol 1997; 71: 360–366.

37.

Tunç

Budak

Effect of cutting conditions and tool geometry on process damping in machining. Int J Mach Tools Manuf 2012; 57: 10–19.

38.

Zou

Sun

, et al. A numerical study of rotor eccentricity and dynamic load in induction machines for motor current analysis based diagnostics. maint reliab cond monit 2021; 1: 71–86.

39.

Blodt

Chabert

Regnier

, et al. Mechanical load fault detection in induction motors by stator current time-frequency analysis. IEEE Trans Ind Appl 2006; 42: 1454–1463.

40.

Blodt

Regnier

Faucher

Distinguishing load torque oscillations and eccentricity faults in induction motors using stator current wigner distributions. IEEE Trans Ind Appl 2009; 45: 1991–2000.

41.

Zhang

Mansaf

, et al. Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis. Mech Syst Signal Process 2017; 94: 202–213.

42.

Zou

Huang

, et al. Investigation into the modulation characteristics of motor current signals in a belt transmission system for machining monitoring. Appl Sci 2022; 12(19): 10088.

Modulation signal bispectrum analysis of motor current signals for online monitoring of turning conditions

Abstract

Keywords

Introduction

Methodology

Turning forces

Influence of mechanical load oscillations on the current signal

Detection algorithm

Numerical analysis of sideband responses

Sideband responses for static loads

Sideband responses for dynamic loads

Experimental validation

MSB phase results

MSB magnitude results

Comparison analysis

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iDs

Data availability

References