Sensor fusion for monitoring machine tool conditions

Bahr, Bo von; Motavalli, Saeid; Arfi, Tanvir

doi:10.1080/095119297131066

Cited by 42 publications

(18 citation statements)

References 1 publication

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“…14 Due to noncontinuous mode of the cutting process and run-out effects in milling operations, vibration signals may not be as reliable as cutting forces and AE signals. 11,15 This exhibits that according to frequency coverage of sensors and complexity of burr formation mechanism, AE and dynamometer sensors seem to perform better when used in milling burr size prediction models. Surprisingly, only few studies 10,16,17 evaluated the correlation between burr size and computed information from AE and/or cutting force signals.…”

Section: Introductionmentioning

confidence: 92%

Burr formation and correlation with cutting force and acoustic emission signals

Niknam

Songmené

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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The principle objective of this work is to present a methodology to evaluate the correlation between burr size attributes (thickness and height) and information computed from acoustic emission and cutting forces signals. In the proposed methodology, cutting force and acoustic emission signals were recorded in each cutting test, and each recorded original acoustic emission signal was segmented into two sections that correspond to steady-state cutting process (cutting signal) and cutting tool exit from the work part (exit signal). The dominant acoustic emission signal parameters including AE max and AE rms were computed from each segmented acoustic emission signal. The maximum values of directional cutting forces (F X , F Y and F Z ) were also measured in each trial. The experimental verification was conducted on slot milling operation which has relatively more complicated burr formation mechanism than that in many other traditional machining operations. Among slot milling burrs, the top-up milling side burrs and exit burrs along up milling side were largest and thickest burrs which were studied in this work. To evaluate the correlation between signal information and burr size, the computed signal information (5 parameters) and their interaction effects (10 parameters) were used to construct the input parameters of the multiple regression fitted models. Statistical methods were then used to assess the adequacy of individual input parameters and signal information. Using the acoustic emission and cutting force signals information in the input layer of multiple regression models, a high correlation was observed between the predicted and observed values of burr size. It was exhibited that due to complex burr formation mechanism in milling operation and strong interaction effects between cutting process parameters, no systematic relationship can be formulated between the milling burrs.

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Section: Introductionmentioning

confidence: 92%

Burr formation and correlation with cutting force and acoustic emission signals

Niknam

Songmené

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…An example for such a method is the direct measurement of a wear area at a tool's flank by means of a CCD camera. There are also some approaches which combine direct and indirect techniques: Bahr et al [4], for example, combine an on-line, indirect technique (tool fracture detection based on vibration signals) with an offline, direct technique (wear area measurement by means of a CCD camera). Due to the disadvantages of intermittent systems (in particular due to the additional loss of time [24]), mainly continuous methods have been investigated in the past years.…”

Section: Selection Of Relevant Publicationsmentioning

confidence: 99%

On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

Sick

2002

Mechanical Systems and Signal Processing

377

143

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“…The tool degradation is a natural phenomenon by its normal usage. Consequently, the tool performance gradually deviates from its nominal level (Bahr et al 1997). Tool degradation is caused by mechanisms such as abrasion, adhesion, diffusion and chemical wear.…”

Section: Introductionmentioning

confidence: 99%

Integration of product quality and tool degradation for reliability modelling and analysis of multi-station manufacturing systems

Jin

Pan

et al. 2009

International Journal of Computer Integrated Manufacturing

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The degradation of a tool component at an upstream station may cause the deterioration of the dimensional quality of a downstream workpiece. Meanwhile, the tool failure and its induced system reliability are affected by the incoming product quality from an upstream station. Quality and reliability interaction is thus introduced to address this interdependence in the reliability modeling and analysis of a multi-station manufacturing system. Moreover, an innovative quality and reliability integrated model is developed to describe the complicated propagation and transmission process of the interaction, as well as the information integration between the tool reliability and the product quality across the stations. Three factors are incorporated into the model, namely the tool degradation, the product quality, and the tool failure. An industrial case of a four-station machining process is used to demonstrate the importance and effectiveness of the proposed analytical procedures. Results indicate that the system reliability will be evidently overestimated without consideration of the quality and reliability interaction. Moreover, the presented integrated model suggests early maintenance schedule for the tool failure and its associated machine downtime.

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Sensor fusion for monitoring machine tool conditions

Cited by 42 publications

References 1 publication

Burr formation and correlation with cutting force and acoustic emission signals

Burr formation and correlation with cutting force and acoustic emission signals

On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

Integration of product quality and tool degradation for reliability modelling and analysis of multi-station manufacturing systems

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