Detection for Cutting Tool Fracture by Acoustic Emission Measurement

Moriwaki, Toshimichi; Okushima, Keiji

doi:10.1016/s0007-8506(07)61291-8

Cited by 67 publications

(14 citation statements)

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“…It is generally agreed that the continuous-type AE signals are associated with plastic deformations and tool wear during metal cutting, while the burst-type signals are observed during crack growth inside the material. Additionally, tool fracture, chip breaking, chip impacts, or chip tangling generate a bursttype AE signals [13][14][15]. As the chip formation is accomplished with crack growth, chip break, and chip removal, it is believed that the chip formation produces a transient burst AE signal during metal cutting.…”

Section: Acoustic Emission In Tool Condition Monitoringmentioning

confidence: 99%

“…The RMS value of burst AE signal due to tool fracture depends on fracture area [16]. However, the AE signals associated with catastrophic tool failure are not influenced significantly by the engagement and disengagement of the tool, depth of cut and feed rate [15]. It is reported that the change in the AE RMS value at the point of tool failure is not significant, especially during interrupted cutting [17].…”

Section: Acoustic Emission In Tool Condition Monitoringmentioning

confidence: 99%

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A new approach to investigate tool condition using dummy tool holder and sensor setup

Bhuiyan

Choudhury

Nukman

2011

Int J Adv Manuf Technol

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The industrial demand for automated machining systems to enhance process productivity and quality in machining aerospace components requires investigation of tool condition monitoring. The formation of chip and its removal have a remarkable effect on the state of the cutting tool during turning. This work presents a new technique using acoustic emission (AE) to monitor the tool condition by separating the chip formation frequencies from the rest of the signal which comes mostly from tool wear and plastic deformation of the work material. A dummy tool holder and sensor setup have been designed and integrated with the conventional tool holder system to capture the time-domain chip formation signals independently during turning. Several dry turning tests have been conducted at the speed ranging from 120 to 180 m/min, feed rate from 0.20 to 0.50 mm/rev, and depth of cut from 1 to 1.5 mm. The tool insert used was TiN-coated carbide while the work material was high-carbon steel. The signals from the dummy setup clearly differ from the AE signals of the conventional setup. It has been observed that time-domain signal and corresponding frequency response can predict the tool conditions. The rate of tool wear was found to decrease with chip breakage even at higher feed rate. The tool wear and plastic deformation were viewed to decrease with the increased radius of chip curvature and thinner chip thickness even at the highest cutting speed, and these have been verified by measuring tool wear. The chip formation frequency has been found to be within 97.7 to 640 kHz.

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Section: Acoustic Emission In Tool Condition Monitoringmentioning

confidence: 99%

Section: Acoustic Emission In Tool Condition Monitoringmentioning

confidence: 99%

A new approach to investigate tool condition using dummy tool holder and sensor setup

Bhuiyan

Choudhury

Nukman

2011

Int J Adv Manuf Technol

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“…In the direct method, tool wear state can be determined based on electrical resistance, optical analysis, or chemical analysis [6]. In the indirect method, wear state can be evaluated according to cutting force [7], torque, temperature, acoustic emissions [8], and vibration [9]. Therefore, the indirect method is more suitable than the direct method for the on-line monitoring of tool wear because it does not interrupt the cutting process.…”

Section: Introductionmentioning

confidence: 99%

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

Jessy¹,

Dinakaran

Kumar³

2015

J Mech Sci Technol

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Glass fiber-reinforced polymers (GFRPs) are a unique class of materials in machining because unlike metals, they are not homogeneous. Given the inhomogeneous and anisotropic nature of composite materials, their machining behavior differs in many respects from that of metal machining. With metallic materials, the cutting force is uniform during drilling. Given GFRPs, drill bits are subject to variable cutting forces, whose responses to machining vary significantly. During GFRP drilling, heavy vibration is undesirable and results in rapid tool wear. The cutting condition can be controlled effectively to avoid heavy vibration by applying the appropriate cutting parameters. Hence, the most suitable cutting condition must be selected for proper process control. This study investigates the influence of vibration as a result of changes in cutting condition, drill diameter, and the number of holes. This influence is continuously monitored by the vibration signals generated during GFRP drilling with a TiN/TiALN-coated carbide drill bit. Experiments were conducted on a computer numerical control milling machine. The input parameters used for various cutting conditions were spindle speed and feed. The vibration signal was measured with an accelerometer, and its fluctuations were examined by a non-linear technique called recurrence quantification analysis (RQA). The influence of these fluctuations on vibration and their effect on tool life were also determined using RQA parameters. These parameters include percent recurrence, percent determinism, and percent laminarity. They have been used to study the effect of continuous fluctuations in vibration signals during drilling. This study confirmed that the RQA technique has potential for use in the investigation of a dynamical system.

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“…However, most of these previous works were focused on sensing of the tool wear rather than detection of the tool failure. The detection of tool failure, which includes cracking, chipping, and fracture of the tool during cutting, is more important than sensing tool wear for the following reasons: (1) tool failure is a stochastic process and is more difficult to predict and detect than tool wear, and (2) tool failure is apt to cause fatal damage to the product (Moriwaki 1980). During actual production, bandsaw teeth are sometimes damaged by sawing pieces of metal, small stones, and unusually hard knots.…”

Section: Introductionmentioning

confidence: 99%

Atomatic detection of damaged bandsaw teeth during sawing

Zhu¹,

Tanaka²,

Ohtani³

2002

Holz als Roh- und Werkstoff

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This paper presents an on-line method for detecting damaged teeth in the bandsaw using acoustic emission (AE) signal energy. The method is based on an analysis of differences in AE energies generated by normal and damaged teeth during sawing. Because of the difference in the amount of sawing, the AE energy was low for sawing by the damaged tooth and high for sawing by the normal tooth immediately after the damaged tooth. The ratio of AE energy for two successive teeth, a damaged tooth and the following normal tooth, was much greater than 1, whereas the ratio of AE energy for two successive normal teeth was close to 1. The possibility of detecting the damaged tooth was investigated for the change of sawing conditions and workpiece thickness. The results demonstrate that the technique using the AE energy ratio for two successive teeth is effective for on-line detection of damaged bandsaw teeth. Automatisches Erkennen von beschädigten Bandsägezähnen während des SägensEine on-line-Methode wird vorgestellt zum Erkennen von beschädigten Sägezähnen während des Sägens. Die Methode nutzt dazu die unterschiedlichen akustischen Emissionssignale (AE) von normalen und beschädigten Sägezähnen während des Sägens. Aufgrund der unterschiedlichen Schnittleistungen waren die AE-Signale bei beschädigten Sägezähnen niedrig,; bei normalen Sägezäh-nen waren sie erhöht unmittelbar nach einem beschädig-ten Zahn. Das Verhältnis der AE-Emissionen zweier aufeinanderfolgender Zähne lag deutlich über 1, wenn ein normaler Zahn auf einen beschädigten folgt. Für zwei aufeinanderfolgende normale Zähne liegt das Verhältnis nahe bei 1. Die Möglichkeiten, defekte Zähne zu erkennen wurden für unterschiedlichen Sägebedingungen und Werkstückdicken untersucht. Die Ergebnisse zeigen, daß diese Methode on-line eingesetzt werden kann, um beschädigte Zähne zu erkennen.

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Detection for Cutting Tool Fracture by Acoustic Emission Measurement

Cited by 67 publications

References 1 publication

A new approach to investigate tool condition using dummy tool holder and sensor setup

A new approach to investigate tool condition using dummy tool holder and sensor setup

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

Atomatic detection of damaged bandsaw teeth during sawing

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