During resistance spot welding, the welding current is the most important process parameter, which determines the welding heat input and then has a great influence on the welding quality. In present study, the CR590T/340YDP galvanized dual phase steel widely used as automobile material was carried out using resistance spot welding. The effect of welding current on the weld formation, microstructure, and mechanical properties was studied in detail. It was found that the quality of weld appearance decreased with the increase of welding current, and there was a Zn island on the weld surface. The microstructure of the whole resistance spot welded joint was inhomogeneity. The nugget zone consisted of coarse lath martensite and a little of ferrite with the columnar crystal morphology, and the microstructure of weld nugget became coarser when the welding current was higher. There was an optimum welding current value and the tensile strength reached the maximum. This investigation will provide the process guidance for automobile body production.

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Streaming time series retrieval (TSR) has been widely concerned in academia and industry. Considering the large volume, high dimensionality and continuous accumulation features of time series, there is limited capability to perform in-depth similarity searching directly on the raw time series data. Therefore, time series representation, which can provide the dimension reduction-based approximate results for the raw data, should be utilized in the first step for streaming TSR. However, the existing representation-based TSR methods mainly have two limitations: on the one hand, the representation efficiency of the current methods is too slow to adapt for real-time streaming time series representation; on the other hand, the retrieval efficiency of them is also not ideal, and thus fails to recognize the specific given sequence patterns on the streaming data effectively. In this paper, we present an efficient retrieval method on streaming time series. Concretely, our method can incrementally represent the features of streaming data to automatically prune the corresponding dissimilar sequences and retain the most similar candidates for efficient one-pass searching. Extensive experiments on real world datasets have been conducted to demonstrate the superiority of our method.