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Abstract: Aiming at on-line monitoring of welding quality, the time
and frequency characteristics are analyzed of the arc sound signals in
short circuit gas metal arc welding (GMAW). The concept of tone channel
and its equivalent electrical model are suggested. The radical basis
function (RBF) neural networks are applied for on-line pattern
recognition of the gas-lack in welding, in which the input vectors of
samples are constructed by the linear prediction coding (LPC)
coefficients of sound signals. The research indicates that, the arc
sound presents a ringing series of that occurs at the end of short
circuit transfer or moment of arc re-ignition, and its energy mostly
distributes in frequency band below 10 kHz. The arc tone channel is a
time dependent distributed parameters system, of which the transmission
properties depend upon many physical and geometrical factors of the arc
and surroundings, and is excited by the sound source that generates from
the change of arc energy, so that results in the sound. The tone channel
can be equated with a time dependent coefficients digital filter, and
can be represented parametrically with the LPC model of arc sound. The
arc sound and its parametric model are valuable in on-line monitoring
and controlling of welding quality.
Key
words: Arc sound Time and frequency analysis Linear
prediction coding (LPC) model Radical basis function (RBF) neural
network
Gas metal
arc welding (GMAW) Quality monitoring
CLC No: TG403
国家自然科学基金资助项目(50275028).
Received 20040619, received in revised form 20050406
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