Identification of noise sources of forge hammers during production: An application of residual spectrum techniques to transients

doi:10.1016/S0022-460X(81)80172-1

Journal of Sound and Vibration

Volume 77, Issue 3, 8 August 1981, Pages 357-374

https://doi.org/10.1016/S0022-460X(81)80172-1 Get rights and content

Three gravity-drop, four-piece forge hammers are analyzed to determine the principal structural sources of impulsive noise radiation during production forging. A three-input single-output model is used to describe the hammer system, based on “input” signals taken from one column, the anvil and the ram accelerations, and one “output” signal taken from the sound pressure at the operator's position. The frequency response functions between the elemental vibrations and the sound pressure signal are determined from these signals by residual spectrum techniques. For the three hammers studied, the analysis shows that the ram is a major source of acoustic energy radiated to the oeprator's position, while the columns and anvil radiate between 1 and 4 dB less energy. The three-input model is then used to estimate the effect of acoustic shrouds on the sound energy radiation. The results indicate that it may be necessary to treat all the structural elements before significant reductions can be achieved, due mainly to the high degree of interaction between the components. The residual spectrum derived from this model indicates that the three input model may not be totally adequate for all production hammers; it may be necessary to develop higher-input models, with use of data from more transducers located to sense a higher number of contributing vibration modes. However, in this study the three-input model accounted for 73–82% of the sound energy density at the operator's position.

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Citation Excerpt :
Frequency domain input–output models have a long history of utilization for separation of the effects of multiple noise and vibration sources from a measured output [1–3]. The noise emanating from the various structural components of both industrial punch presses and closed die forging hammers from the respective striking operations have been evaluated [1,2]. The transfer path identification of airborne and engine noise in an automobile is discussed in [3].
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A compact formulation for conditioned spectral density function analysis by means of the LDL<sup>H</sup> matrix factorization
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Determination of priority among correlated inputs in source identification problems
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Frequency response function technique and partial coherence function technique are two typical approaches in noise source identification of multiple-input/single-output systems. In the second approach, which is used in cases where the inputs are correlated to each other, determining the priority of the input records is a key factor since the priority determines the way in which the results are interpreted. This paper presents a method for determination of priority among inputs when applying the partial coherence function technique to source identification problems. The basic idea is that, if one signal of any two correlated signals causes the other signal, then the causality can be identified by observing two impulse response functions estimated in the negative time region. One impulse response function is obtained by the inverse Fourier transform of the frequency response function, where the first signal is assumed as input and the second signal as output. The other impulse response function is obtained by assuming that the second signal is the input and the first signal is the output. The method was applied to a simulated two-inputs/one-output system in which the inputs were correlated to each other. It is expected that the proposed method will work as a successful means of determining priority among multiple inputs in source identification problems.
Bias errors in estimating frequency response and coherence functions from truncated transient signals
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An estimation bias error is known to exist when spectra are estimated from truncated responses to transient signals. Expressions for this bias error are developed for zeroth, first and second order systems excited by a burst of white noise, with varying degrees of truncation of the response signal. A series of experiments confirms the theoretical results. For the zeroth order system the effects of truncation are identical for both the frequency response and coherence functions, with a bias error proportional to the fraction of output signal truncation. For first and second order systems, it is shown that the frequency response functions can be accurately estimated with 5% provided that one and three time constants, respectively, of the response signal are captured after the excitation has ceased. On the other hand, the bias of the output autospectrum and the coherence function are much more sensitive to the degree of truncation. Estimating these functions within 5% may require capturing seven or more time constants after the excitation has ceased. Upper bound error limits are developed from the bias functions to help establish data capture requirements when analyzing real systems under varying degrees of signal truncation. It is shown that transient excitation requires significantly shorter capture time per record than continuous excitation to provide the same degree of estimation accuracy.
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An investigation is reported of the bias effect introduced by time delays when frequency response and coherence functions are estimated from windowed time samples. The magnitude of the bias error for both functions is shown to be dependent on both the nature of the data capture window and the amount of delay. The bias is found to be more severe for random data processed through a Hanning window than through a “boxcar” window. For transient signals that are fully captured through a boxcar window, the bias error is shown to be zero. Experimental investigation verifies the derived estimates for both boxcar and Hanning windows.
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Identification of noise sources of forge hammers during production: An application of residual spectrum techniques to transients

Journal of Sound and Vibration

Journal of Sound and Vibration

Journal of Sound and Vibration

Journal of Sound and Vibration

Control of noise in forging operation

Forging Industry Educational and Research Foundation, Cleveland, Ohio, U.S.A., Industrial Noise Control Research Project, Quarterly Reports

Engineering guideline to forging noise control

Forging Industry Educational and Research Foundation, Cleveland, Ohio, U.S.A.

Final report for the development project: noise-attenuated forging hammers. (Translation.)

Institut für Messtechnik in Machinenbau, T.-U. Hannover

Impulformige Luftdruckwellen durch Gesenkbewegung bei Schmiedehammern

Institut für Messtechnik in Machinenbau, T.-U. Hannover

The fundamentals of noise generated in impact forming machines

Noise due to air ejection from clash surfaces of impact forming machines

Acoustodynamics of the longitudinal collinear impact of finite elastic cylinders

American Society of Mechanical Engineers, Paper 76-WA/DE-19

Billet expansion as a mechanism for noise production in impact forming machines

Journal of Sound and Vibration

Impact-induced industrial noise

Noise Control Engineering

Modeling of diesel engine noise using coherence

Society of Automotive Engineers, Paper 790362