Experimental Analysis of Chatter Vibration in End-Milling Using Laser Doppler Vibrometers

Nakagawa, Heisaburo; Kurita, Yutaka; Ogawa, Keiji; Sugiyama, Yuji; Hasegawa, Hideyasu

doi:10.20965/ijat.2008.p0431

Cited by 23 publications

(4 citation statements)

References 5 publications

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“…After that the technique was devolved as LDV. Nakagawa et al (2008), Prasad et al (2010), andVenkata Rao et al (2013) used the LDV to measure vibrations of rotating cutters and work pieces. They concluded that the use of LDV is easy and it takes less time to measure vibration of work piece.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Rao¹,

Murthy

2016

J Intell Manuf

101

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In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.

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Section: Introductionmentioning

confidence: 99%

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Rao¹,

Murthy

2016

J Intell Manuf

101

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“…They use optical method such as laser displacement sensors for vibration detection in the milling process. The authors of [14] propose the measurement technique suitable for monitoring vibration, chatter and tool wear in a rotating spindle [15].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

Ostaševičius

Jurenas

Augutis

et al. 2016

Int J Adv Manuf Technol

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An effective system for monitoring the wear of the machine tool inserts could significantly contribute to saving costs in manufacturing. One of the most recent and popular of effective monitoring methods revolves around the use of sensing technologies for indirect estimation of tool wear. The sensory information is difficult to collect from machine tools due to the extremely poor signal-to-noise ratio of the relevant tool wear-related information because the milling operation is of interrupted nature since the work piece is in contact with the tool edge several times per second. Another issue is the varying thickness of the chip during the penetration of the work piece. Yet, many challenges still impede the practical application of this method for industrial environment. Therefore, the paper presents a method that could solve these challenges especially in industrial environment. The proposed selfpowered wireless sensor node is integrated in the structure of the cutting tool. The voltage generated from the cutting tool vibrations of the harvester exponentially rises when the capacitor is fully charged and wireless signal sent to the receiver. As the intensity of energy accumulation depends on the state of the cutting tool wear, it indicates and detects the tool condition. The proposed technique could be useful for the identification of the cutting tool quality and the relative tool-work piece position. The information about the variation of tool wear is beneficial for helping the manufacturers to control the cutting process, to minimize the product cost as well as to improve the machining quality and efficiency.

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“…LDVs will observe the chatter behavior of the end-mill shank and the chatter vibrations at the high cutting speed. 14 A fast Fourier transform preprocessor is used for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. The AOE signals can be transformed into time domain with different frequency zones by the fast Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on tool condition monitoring in boring of AISI 316 stainless steel

Rao

Murthy

Rao

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this article, effect of cutting parameters, namely, cutting speed, feed rate and tool nose radius, on the tool life was examined experimentally. The experiments were performed on boring of AISI 316 steel with cemented carbide tool inserts. Design of experiments was prepared, and eight experiments were performed with two levels of the cutting parameters. The effects of cutting parameters were analyzed by evaluating the amplitude of workpiece vibration, surface roughness and volume of metal removed. The experimental data were taken for evaluation of tool life for a flank wear of 0.6 mm in all the eight trials. A laser Doppler vibrometer was used for online data acquisition of workpiece vibration, and a high-speed fast Fourier transform analyzer was used to process the acousto-optic emission signals for the workpiece vibration. Taguchi, analysis of variance and regression analysis methods were used to identify significant cutting parameters affecting the workpiece vibrations, surface roughness and volume of metal removed. All selected analysis methods used in this study predicted similar cutting parameter.

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Experimental Analysis of Chatter Vibration in End-Milling Using Laser Doppler Vibrometers

Cited by 23 publications

References 5 publications

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

Experimental study on tool condition monitoring in boring of AISI 316 stainless steel

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