Tool strain–based wear estimation in micro turning using Bayesian networks

Mandal, Soumen; Sharma, Vimlesh Kumar; Pal, Aniruddha; Nagahanumaiah,

doi:10.1177/0954405416654420

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Measurement of tool temperature and strain near to cutting edge in micro turning has been attempted recently using FBG sensors. 11,12 However, variations of these two quantities with size-effect influence (cutting, ploughing and slipping) which is essential for micro turning as opposed to conventional machining operations are not available. In order to bridge the existing gaps in and to analyze the variation of strain and temperature with varying conditions due to size-effect, which could facilitate better tool state monitoring and surface integrity assessment in micro turning, we propose a generic methodology for the same in this research.…”

Section: Existing Literaturementioning

confidence: 99%

Section: Existing Literaturementioning

confidence: 99%

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Investigations on size-effect dependent strain and temperature in micro turning near to the cutting edge

Mandal

Hanumaiah

2019

Journal of Micromanufacturing

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This research presents an investigation for understanding machining physics by assessing temperature and strain near (126 μm) to the cutting edge of tool tip in micro-turning process. Fiber Bragg grating (FBG) sensors were attached near to the cutting edge of the micro-turning tool and experiments with varying depth of cut were conducted to record the strain and temperature simultaneously on an indigenous developed micro-machining centre 'Multi-Fab'. It was observed that with varying depth of cut different mechanisms that is, cutting, ploughing and slipping were dominant due to size-effect. In slipping the temperature and strain fluctuates around a fixed value due to elastic deformation of the work. The recorded strain and rise in temperature during cutting operation were found to be about four times and twice, respectively as compared to ploughing. Material hardness and rate of heat dissipation also play a major role in strain and temperature near to the cutting edge as validated from the experiments. The observations found from the experiments can enable appropriate process control in micro turning by monitoring strain and temperature near to the cutting edge.

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Section: Existing Literaturementioning

confidence: 99%

Section: Existing Literaturementioning

confidence: 99%

Investigations on size-effect dependent strain and temperature in micro turning near to the cutting edge

Mandal

Hanumaiah

2019

Journal of Micromanufacturing

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“…The details of the mathematical operations can be found in the reference. 21 The algorithm computes the state-space model for the given inputs and outputs of a MIMO system as stated in equations (1) and (2) x…”

Section: System Identification Using N4sid Algorithmmentioning

confidence: 99%

“…The physics of material removal is dependent on a number of factors such as machining parameters, experimental conditions, effect of micro-structures and material properties. 1,2 The dependence of the tool–work conditions during and post machining on multiple parameters makes the machining physics complex. Physical quantities such as tool strain, temperature, machining vibration and cutting forces change the machining physics.…”

Section: Introductionmentioning

confidence: 99%

Time-varying process model for intelligent prediction of strain and temperature in micro-turning process

Mandal

Pal

Nagahanumaiah

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Time-varying process models for micro-machining processes are important as they aid in control of machining parameters. In this research, a state-space-based process model for the temperature and strain generated near the cutting edge of the tool tip is identified using system identification approach. Fiber Bragg grating sensors were placed rigidly near the cutting edge of the tool tip in a micro-turning setup. Subsequently, micro-turning operations were carried out on aluminum and mild steel. The computer numerically controlled program was such that the machining parameters (feed velocity, depth of cut and RPM) change with machining time. The time-varying machining parameters act as inputs to the model, and the dynamic values of strain and temperature serve as model output. A state-space model was generated using the experimental data. Subsequently, a Kalman filter was used to intelligently predict the values of strain and temperature at the cutting edge of tool tip in advance using the model parameters identified by state-space modeling. Experimental results confirm that the time-varying model and the Kalman filter proposed in this research are effective in predicting the strain and temperature in advance with high accuracy. The maximum error in prediction of temperature was 0.4 °C, whereas for strain prediction, the maximum error was 0.3µ∈.

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