A study on application of multi‐sensor information fusion in pulsed GTAW

Abstract

PurposeWelding sensor technology is the key technology in welding process, but a single sensor cannot acquire adequate information to describe welding status. This paper addresses arc sensor and sound sensor to acquire the voltage and sound information of pulsed gas tungsten arc welding (GTAW) simultaneously, and uses multi‐sensor information fusion technology to fuse the information acquired by the two sensors. The purpose of this paper is to explore the feasibility and effectiveness of multi‐sensor information fusion in pulsed GTAW.Design/methodology/approachThe weld voltage and weld sound information are first acquired by arc sensor and sound sensor, then the features of the two signals are extracted, and the features are fused by weighted mean method to predict the changes of arc length. The weights of each feature are determined by optional distribution method.FindingsThe research findings show that multi‐sensor information fusion technology can effectively utilize the information of different sensors and get better result than single sensor.Originality/valueThe arc sensor and sound sensor are first used at the same time to get information about pulsed GTAW and the fusion result shows its advantages over single sensor; this reveals that multi‐sensor fusion technology is a valuable research area in welding process.

Study on the Relationship Between the Energy in Most Effective Frequency Range of Arc Sound Signal and the Change of Arc Height in Pulsed Al Alloy GTAW Process

“…where s1, s2, s3, s4, s5, s6, s7, s8 represent signals in different nodes (3, 0), (3, 1), (3,2), (3,3), (3,4), (3,5), (3,6), (3,7), (3,8). Ei represents corresponding energy of si, i is integer from 1 to 8.…”

“…Arc sound, as non-contact vibration source signals, which generates from arc and molten pool heat vibration, contains a large amount of information of penetration states, and is not susceptible to the changes of weldment. Arc sound signals can effectively reflect pool height information and welding quality [6][7][8]. At present, the acquisition processing and analysis arc sound signal have achieved certain results in laser welding, CO 2 welding, MIG welding, spot welding, and other fields.…”

Research on detection of welding penetration state during robotic GTAW process based on audible arc sound

“…And the ANN is chose for the modeling method because of its nonlinear mapping for high-resolution information compression, the complex classification problem in welding statement pattern recognition is transferred to feature processing of all kinds of arc signals (Ma et al, 2006;Sukhomay et al, 2008;Lv and Chen, 2011). Many attempts have been made to correlate the arc signals to the weld quality using ANN (Chen et al, 2010a(Chen et al, , b, 2009Huang and Radovan, 2011).…”

Sample Entropy-Based Approach to Evaluate the Stability of Double-Wire Pulsed MIG Welding

“…Xue et al analyzed the effects of current and voltage on welding stability from the perspective of statistics and spectrum [16,17]. Li et al and Chen et al established a multi-information acquisition system to collect a variety of electrical signals for analyzing the welding process [18,19]. Chen et al considered that relatively stable welding process can be obtained only under optimum operations conditions by analyzing the effect of current waveform on the stability of short circuiting transfer process [20].…”