In situ monitoring methods for selective laser melting additive manufacturing process based on images-A review

Selective laser melting(SLM)has been widely used in the fields of aviation,aerospace and die manufacturing due to its ability to produce metal components with arbitrarily complex shapes.However,the instability of SLM process often leads to quality fluctuation of the formed component,which hinders the further development and application of SLM.In situ quality control during SLM process is an effective solution to the quality fluctuation of formed components.However,the basic premise of feedback control during SLM process is the rapid and accurate diagnosis of the quality.Therefore,an in situ monitoring method of SLM process,which provides quality diagnosis information for feedback control,became one of the research hotspots in this field in recent years.In this paper,the research progress of in situ monitoring during SLM process based on images is reviewed.Firstly,the significance of in situ monitoring during SLM process is analyzed.Then,the image information source of SLM process,the image acquisition systems for different detection objects(the molten pool region,the scanned layer and the powder spread layer)and the methods of the image information analysis,detection and recognition are reviewed and analyzed.Through review and analysis,it is found that the existing image analysis and detection methods during SLM process are mainly based on traditional image processing methods combined with traditional machine learning models.Finally,the main development direction of in situ monitoring during SLM process is proposed by combining with the frontier technology of image-based computer vision.

CLIMATIC FEATURES OF SEA TEMPERATURE OF WARM POOL AND RELATIONSHIP WITH SST OF ITS ADJACENT REGIONS

In this paper,climatic features of sea temperature of western Pacific warm pool and the relationship with sea surface temperature (SST) of its adjacent regions are analyzed based on the observed sea temperature on vertical cross section along 137°E in western Pacific,the monthly mean SST of Xisha Station in South China Sea and the global monthly mean SST with resolution of 1°×1°(U.K./GISST2.2).The results indicate that (1) in a sense of correlation.SST of western Pacific warm pool can represent its sea subsurface temperature from surface to 200 m-depth level in winter,and it can only represent sea temperature from surface to 70 m depth in summer.The sea subsurface temperature anomaly of warm pool may be more suitable for representing thermal regime of western Pacific warm pool.The sea subsurface temperature of warm pool has a characteristic of quasi-biennial oscillation.(2)Warm pool and Kuroshio current are subject to different ocean current systems (3)Furthermore,the relationship between SST of Xisha Station and SST of warm pool has a characteristic of negative correlation in winter and positive correlation in summer,and a better lag negative correlation of SST of Xisha Station with sea subsurface temperature of warm pool exists.(4)Additionally,oscillation structure of sea temperature like “a seesaw” exists in between warm pool and Regions Nino3 and Nino4.January (June) maximum (minimum) sea subsurface temperature anomaly of warm pool may serve as a strong signal that indicates maturity phase (development phase) of La Nina (El Nino) event,it also acts as a strong signal which reveals variations of SST of Regions Nino3 and Nino4.