A cyber physical system for tool condition monitoring using electrical power and a mechanistic model

Goodall, Paul A.; Pantazis, Dimitrios; West, Andrew A.

doi:10.1016/j.compind.2020.103223

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…The ability of AC induction motors to serve as mechanical power sensors based on the evolution of the active electrical power is known and is usually exploited in machine tools for: working processes monitoring (e.g., anomalies detection or faults detection on CNC machining processes [ 5 , 6 , 7 ], cutting power measurement in milling [ 8 , 9 , 10 ], drilling [ 11 ] or turning [ 12 ], peak power reduction [ 13 ]), tool condition monitoring [ 14 , 15 , 16 , 17 ], feed drive systems monitoring [ 18 ] or auxiliary systems monitoring [ 19 ]. Some researchers have focused on the monitoring [ 1 , 2 ] and optimization [ 20 ] of transient regimes on machine tools (acceleration-deceleration) using the evolution of the active electrical power.…”

Section: Introductionmentioning

confidence: 99%

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Horodincă

Bumbu

Chitariu

et al. 2023

Sensors

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This paper presents some advances in condition monitoring for rotary machines (particularly for a lathe headstock gearbox) running idle with a constant speed, based on the behaviour of a driving three-phase AC asynchronous induction motor used as a sensor of the mechanical power via the absorbed electrical power. The majority of the variable phenomena involved in this condition monitoring are periodical (machines having rotary parts) and should be mechanically supplied through a variable electrical power absorbed by a motor with periodical components (having frequencies equal to the rotational frequency of the machine parts). The paper proposes some signal processing and analysis methods for the variable part of the absorbed electrical power (or its constituents: active and instantaneous power, instantaneous current, power factor, etc.) in order to achieve a description of these periodical constituents, each one often described as a sum of sinusoidal components with a fundamental and some harmonics. In testing these methods, the paper confirms the hypothesis that the evolution of the electrical power (instantaneous and active) has a predominantly deterministic character. Two main signal analysis methods were used, with good, comparable results: the fast Fourier transform of short and long signal sequences (for the frequency domain) and the curve fitting estimation (in the time domain). The determination of the amplitude, frequency and phase at origin of time for each of these components helps to describe the condition (normal or abnormal) of the machine parts. Several achievements confirm the viability of this study: a characterization of a flat driving belt condition and a beating power phenomenon generated by two rotary shafts inside the gearbox. For comparison purposes, the same signal analysis methods were applied to describe the evolution of the vibration signal and the instantaneous angular speed signal at the gearbox output spindle. Many similarities in behaviour among certain mechanical parts (including their electrical power, vibration and instantaneous angular speed) were highlighted.

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Section: Introductionmentioning

confidence: 99%

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Horodincă

Bumbu

Chitariu

et al. 2023

Sensors

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“…In comparison, the current and power sensors embedded in machine tool will not induce interference in machining, and the monitoring signal is robust to environmental noises and easy to acquire. Therefore, hardware devices for monitoring have gradually shifted from high value-added equipment to low-cost and easy-to-install internal sensors in the present intelligent manufacturing system [9]. The variation of the spindle power could be used to evaluate status of the machine tools, especially for the real-time tool condition changes.…”

Section: Introductionmentioning

confidence: 99%

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

Qiang¹,

Shi²,

Liu³

et al. 2022

Preprint

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Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for milling difficult-to-cut materials. With the degradation of cutting performance, the undesirable tool status could trigger instability in machining process that causes negative effect on machined surface and dimension accuracy of the precision components. Therefore, tool condition monitoring for machining process is imperative. With distinctive characteristics on easy acquisition and non-interference in machining process, the power consumption of machine tool has been proved to be a reliable indicator for reflecting the changes of cutting process in industry. Thus, the present study proposes a novel method for monitoring tool condition using net cutting power consumption. The result indicates that the developed approach performs better than traditional approach which uses original total power to predict tool wear. This can be attributed to the changes of net cutting power consumption is more relevant with tool wear evolution. Furthermore, predicting tool wear under variable cutting parameters is achieved using Gaussian process regression, because it has good generalization performance and also explains the uncertainty of prediction results in the form of probability. This study reveals that low-cost sensor like power meter, can be used as an important supplement to monitor tool condition in the industry, and also provides a research basis for predicting tool wear under different cutting conditions.

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“…In the manufacturing process, with the development of related fields in the past few decades, many TCM methods have been proposed and developed. For example, part of the work attempted to monitor abnormal conditions based on physical models [11,12]. However, the physical model is often very complicated.…”

Section: Introductionmentioning

confidence: 99%

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

2021

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In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

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A cyber physical system for tool condition monitoring using electrical power and a mechanistic model

Cited by 23 publications

References 21 publications

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

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