HFIM

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Representation of a structure-borne noise signal using a classic "amplitude vs. Time ”diagram (1) and as an HFIM process landscape (2). The separation of interference and useful noise is particularly clear here, as a pulse-like signal (3) can be identified immediately in the HFIM display, while it disappears completely in the classic display.

HFIM , acronym for high-frequency pulse measurement ( English high-frequency-impulse-measurement , also HFIM), is a specific method of the structure-borne noise measurement. A frequency analysis (usually by means of a Fourier transformation ) of the recorded sound amplitudes is carried out during the measurement process , so that not only information about the "volume" of the measured process is obtained in real time, but also its frequency distribution. This enables the simple separation of interfering noises and useful signals, even if these useful signals are significantly weaker than the interfering noises. The method is therefore used in the in-line monitoring of industrial production processes that require one hundred percent quality monitoring. HFIM is also used in the area of condition monitoring and tool wear.

Basics

The high-frequency pulse measurement is a calculation algorithm based on discrete signal transformation for the analysis of structure-borne noise signals. It is used to obtain frequency information from any structure-borne sound source. A Fourier series expansion is usually used for this, which is implemented on the software side by means of a Fast Fourier Transformation (FFT). This has the advantage that the frequency information can be calculated very quickly and is therefore available almost instantaneously. In contrast to envelope curve analysis, the classic analysis method of structure-borne noise measurement, the signal is not unfolded and then transformed, but is directly subjected to an FFT. As a result, singular events in particular are represented by the FFT as signals with strongly deviating periodicity (the Fourier transformation of a single pulse is a signal that extends over the entire frequency range considered) and can therefore be easily distinguished from the other "noises" of the process . The signal-to-noise ratio obtained through this analysis step is the main advantage of the HFIM over conventional structure-borne sound analysis methods. Mathematically, it is simply based on the fact that, strictly speaking, the Fourier transform is not defined for one-off, non-periodic signals. The resulting clear highlighting of such signals, which in practice usually indicate disruptive events in a process such as the formation of cracks, is precisely what is desired.

Acoustic detection of cracks in metallic structures

A crack in a metallic structure always results in the destruction of metallic bonds and leads to the failure of a component. A distinction is made between microscopic cracks and macroscopic cracks. Essentially, the sources of the crack initiation are known. These include, in particular, processing-related surface roughness and notches, surface protrusions (protrusions) due to sliding strips, stress increases due to non-metallic inclusions, precipitations and pores as well as grain boundaries.

The HFIM can be used to detect and unambiguously quantify structure-borne noise signals that can be traced back to these creation mechanisms. Due to the elastic deformability of metals, solid-borne sound waves are passed through the grid with both longitudinal and transverse components. When cracks develop, energy is released in the form of heat, kinetic energy through the crack opening and solid-borne sound waves. The solid-borne sound waves are particularly suitable for detection because they can be detected by long-distance transmission and reflection at interfaces at any point on the component surface or elements connected to the component surface. This requires the force-fit connection of the piezo sensors used with the component or tool. The solid-borne sound waves transmitted when cracks develop have a frequency in the ultrasonic range (> 20 kHz) and are acoustically imperceptible to humans. Phenomena like the tin scream have sound frequencies that are <20 kHz, as they stimulate the surrounding air.

Sensor technology

For the high-frequency detection of structure-borne sound waves , a piezoceramic is usually used, as is also used in vibration or position sensors. One main difference here, however, is that an HFIM sensor works without a seismic mass, i.e. does not react to accelerations. A simple accelerometer cannot be used as an HFIM sensor. This is also due to the different electrical circuitry of the piezo element, which in the case of an HFIM sensor is not read out via a charge amplifier , but only voltage pulses are tapped. The coupling of the sensors for structure-borne sound applications is therefore usually done by screwing in, gluing on and by fastening with a holding magnet and must be force-locked.

Applications

Due to its in-line capability, the HFIM is mostly used in the monitoring of industrial production processes. The following procedures form a particular focus:

  • Cold forming : In the field of cold forming, the HFIM is mostly used to detect cracks during the corresponding machining process. Since stress cracks usually arise here, which show a particularly clear impulse characteristic in the structure-borne sound image, the HFIM is the quality assurance tool of choice for many manufacturers, especially in the automotive industry.
  • Machining : Since very fast process analysis methods have to be used in high-performance machining (speeds around 20,000 / min), the HFIM is used here to detect errors in the machining process and to ensure tool monitoring. In this way, tool life can be optimized and typical errors such as chatter marks can be reliably found.
  • Plastic injection molding : Here, HFIM is less used to monitor the injection process than the condition of the tool, the injection mold. In practice, particularly with filigree shapes, there are often defects in the shape, such as the breaking off of individual molded parts, which leads to the production of rejects. With HFIM, such tool defects can be detected as they arise.
  • Welding : Welding seam monitoring using HFIM has the advantage over conventional monitoring systems (current, voltage) that it is not input variables from the welding unit that are used to monitor the process, but the structure-borne noise emissions of the process itself. This creates a much more direct picture of the process This also helps to detect downstream defects such as stress cracks in the cooling phase. Another great advantage is that laser welding processes can be qualified in the same way.

HFIM measuring devices are also used in material science laboratories in order to provide objective limits for the development of cracks in standard expansion tests (e.g. to characterize the plasticity of materials).

literature

  • S. Barteldes, F. Walther, W. Holweger: Rolling bearing diagnosis and detection of white etching cracks with Barkhausen noise and high-frequency pulse measurement. In: AKIDA. 10th Aachen Colloquium for Maintenance, Diagnosis and Plant Monitoring. (= Aachen writings on raw material and disposal technology of the Institute for Machine Technology of the Raw Material Industry. Volume 84). Zillekens, Stolberg 2014, ISBN 978-3-941277-21-2 , p. 435 ff.
  • D. Hülsbusch, F. Walther: Damage detection and fatigue strength estimation of carbon fiber reinforced polymers (CFRP) using combined electrical and high-frequency impulse measurements. In: 6th International Symposium on NDT in Aerospace, 12-14th November 2014, Madrid, Spain.
  • A. Ujma, B. Walder: Tool maintenance at the right time. In: plastics. Edition 2/2013, Carl Hanser Verlag.
  • F. Özkan, D. Hülsbusch, F. Walther: High-frequency impulse measurements (HFIM) for damage detection and fatigue strength estimation of carbon fiber reinforced polymers (CFRP). In: Materials Science and Engineering. Darmstadt, Sept. 2014, pp. 23-25.

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