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The corrosion behavior of 316 stainless-steel with a tantalum (Ta-316) coating deposited using PVD technology was investigated in subcritical environments containing Cl− and Br− at 290 °C and 6 MPa. The corrosion rate of Ta-316 continuously decreased with increasing exposure time, demonstrating its high corrosion resistance. A thin film, mainly composed of Ta 2 O 5 , formed on the surface of the tantalum coating, preventing the diffusion of substrate elements. The corrosion process and mechanism of Ta-316 were also examined. • Corrosion of alloys by Cl- and Br- under subcritical conditions was studied. • The tantalum coating effectively inhibits substrate corrosion. • Outer layers is mainly composed of Ta 2 O 5 , while transition layer is composed of Ni-Fe oxides. • The uniform corrosion induced by Br- trended to be more severe than that induced by Cl- in the short term. [ABSTRACT FROM AUTHOR]

Performance prediction plays a key role in product quality control and quality optimization. However, due to the error transmission in the refrigeration compressor assembly process and the imbalance of data collected during the refrigeration compressor assembly process, existing performance prediction methods cannot accurately extract key features of the assembly process resulting in low prediction accuracy. In this paper, a knowledge graph neural network (AKGNN) model with predictive value calibration is proposed for refrigeration compressor performance prediction in two stages. First, a sequence graph attention feature extraction module is designed to extract key features of the assembly process data to cope with the assembly error propagation. Second, to deal with the imbalanced distribution of compressor data, a predictive value calibration (PC) module is designed to calibrate the output of the first stage. Experimental results demonstrate the proposed method outperformed all compared cutting-edge methods in four evaluated indicators. [ABSTRACT FROM AUTHOR]