Overview of recent advances of process analysis and quality control in resistance spot welding

Sensor technology application is the key for intelligent welding process. Multiple sensors fusion has shown their significant advantages over single sensor which can only provide limited information. In this paper, a feature-level data fusion methodology was presented to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding by means of online arc sound, voltage and spectrum signals. Based on the developed algorithms in time and frequency domain, multiple feature parameters were successively extracted and selected from sound and voltage signals, while spectrum distribution of argon atoms related to seam penetration were carefully analyzed before feature parameters selection. After the synchronization of heterogeneous feature parameters, the feature-level-based data fusion was conducted by establishing a classifier using support vector machine and 10-fold cross validation. The test results indicate that multisensory-based classifier has higher accuracy i.e., 96.5873 %, than single sensor-based one in term of recognizing seam defects, like under penetration and burn through from normal penetration.

Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

Real-time estimation of weld quality from process data is one of the key objectives in current weld control systems for resistance spot-welding processes. This task can be alleviated if the weld controller is equipped with a voltage sensor in the secondary circuit. Replacing the goal of quantifying the weld quality in terms of button size by the more modest objective of indirect estimation of the class of the weld, e.g., satisfactory (acceptable, “normal” button size), unsatisfactory (undersized, “cold” welds), and defects (“expulsion”), further improves the feasibility of the mission of indirect estimation of the weld quality. This paper proposes an algorithmic framework based on a linear vector quantization (LVQ) neural network for estimation of the button size class based on a small number of dynamic resistance patterns for cold, normal, and expulsion welds that are collected during the stabilization process. Nugget quality classification by using an LVQ network was tested on two types of controllers; medium-frequency direct current (MFDC) with constant current controller and alternating current (AC) with constant heat controller. In order to reduce the dimensionality of the input data vector, different sets of features are extracted from the dynamic resistance profile and are compared by using power of the test criteria. The results from all of these investigations are very promising and are reported here in detail.

Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding

Dynamic electrode displacement and force were characterized during resistance spot welding of aluminum alloy 5182 sheets using a medium-frequency direct-current welder. It was found that both electrode displacement and force increased rapidly at the beginning of the welding stage and then at a reducing rate. Rates of increase in electrode displacement and force were both proportional to welding current. And both electrode displacement and force experienced a sudden drop when weld metal expulsion occurred. However, the rate of increase in electrode displacement did not reach zero during welding even for joints with sufficient nugget diameter while electrode force peaked when a large nugget diameter was produced. Possible strategies for process monitoring and control were also discussed.

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Dynamic Electrode Force and Displacement in Resistance Spot Welding of Aluminum

Although many different sensors have been developed for monitoring the resistance spot welding (RSW) process, no single sensor has been found to be capable of reliably detecting all defects associated with the process. A number of sensor systems are considered to hold promise of providing information about the process. In this paper, an investigation of various sensor systems for RSW is presented and the signals are analyzed and correlated with nugget formation and growth. These include a fiber-optic displacement sensor, an acoustic emission sensor, a force transducer, and the current and voltage measurements, which are set up to simultaneously monitor the welding process. Of these sensor systems, force signals are found to provide the most information on nugget formation.

C.-S. Chien

Papers

Investigation of monitoring systems for resistance spot welding