Experimental investigation of tool wear using vibration signals: An ANN approach

Upase, Ritesh; Ambhore, Nitin

doi:10.1016/j.matpr.2020.04.454

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…FFT analysis is also widely used in the monitoring and fault detection of the vibration state of machine elements in constant motion such as bearings and gears. FFT analyzes, which have an important place in space, aviation and automotive industry, are performed by using multiples of two (512, 1024, 2048) samples in the time block [28].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Güvenç

Bilgiç

Mıstıkoğlu

2021

Preprint

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In recent years, with the development of sensor technologies, communication platforms, cyber physical systems, storage technologies, internet applications and controller infrastructures, the way has been opened to produce competitive products with high quality and low cost. In turning, which is one of the important processes of machining, chatter vibrations are among the biggest problems affecting product quality, productivity and cost. There are many techniques proposed to reduce chatter vibrations for which the exact cause cannot be determined. In this study, an active vibration control based on Sliding Mode Control (SMC) has been implemented in order to reduce and eliminate chatter vibration, which is undesirable for the turning process. In this context, three-axis acceleration data were collected from the cutting tool during the turning of Ti6Al4V. Finite Impulse Response (FIR) filtering, Fast Fourier Transform (FFT) analysis and integral process were carried out in order to use the raw acceleration data collected over the system in control. The system was modeled mathematically and an active control block diagram was created. It was observed that chattering decreased significantly after the application of active vibration control. The surface quality formed by the amplitude of the graph obtained after active control has been compared and verified with the data obtained from the actual manufacturing result.

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Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Güvenç

Bilgiç

Mıstıkoğlu

2021

Preprint

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“…Characteristics. e vibration signal of tool wear is mainly in the low-frequency band (no more than 8 kHz) [24]. Considering the integrity of the collected signal, the sensor with the frequency range of 0.7-13 kHz and sampling speed of 250 k/s is selected to collect the signal, and the db8 is selected as the wavelet base to decompose the collected signal by 6-level wavelet packet, and then the frequency difference of each frequency band is reduced to 1.95 kHz.…”

Section: Selection Of Vibration Signalmentioning

confidence: 99%

Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network

Gui

Xie

2021

Shock and Vibration

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The multi-information data acquisition system of tool wear condition of CNC lathe is built by acquiring the acoustic emission and vibration acceleration signals. The data of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing under the conditions of different tool wear degrees and different cutting conditions are acquired and analyzed using the orthogonal experimental method. The optimum characteristic frequency band of acoustic emission and vibration acceleration signals was extracted by the wavelet envelope decomposition method so as to recognize tool wear condition as the characteristic parameters. The characteristic information of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing was fused. In addition, the intelligent recognition of tool wear condition during the process of machine tool processing was researched.

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“…[7] Hence, it is more suitable for industrial applications. On the other hand, vibration signal has greater advantages in terms of correlation with cutter wear, cost, and installation conditions compared with other indirect methods [8] , such as cutting force, spindle power/current, acoustic emission, temperature, etc. In addition, the multi-sensor fusion method is one of the hot research trends in recent years.…”

Section: Introductionmentioning

confidence: 99%

Vibration signal acquisition and extraction for cutter wear monitoring

Zhou,

Li,

Shen

et al. 2024

J. Phys.: Conf. Ser.

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The cutter wear state is key in manufacturing. During the machining, the vibration signal of the tool spindle will change with the increase of cutter wear, which provides an effective method for cutter wear monitoring. However, due to the popularity of machining centers, the machining trajectory of a cutter is extremely complex in practical production. The complexity includes a single process containing multiple work steps, a step containing random and long non-cutting time, etc., making it difficult to acquire and extract the specific step signal as the effective signal for cutter wear monitoring. Therefore, this paper proposes a system that can synchronously acquire machine parameters and tool spindle vibration signals. Then, two algorithms—the cutting signal recognition algorithm and the step signal similarity detection algorithm—are proposed. The former algorithm is based on sliding window energy, which can accurately identify the instant the cutter begins to cut the workpiece. The step signal similarity detection algorithm based on the Edit Distance on Real (EDR) sequence ensures the correctness of the step extraction. The experimental results show that the system has efficient acquisition of vibration signals and correct step signal extraction, providing a data basis for cutter wear monitoring.

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Experimental investigation of tool wear using vibration signals: An ANN approach

Cited by 13 publications

References 7 publications

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network

Vibration signal acquisition and extraction for cutter wear monitoring

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