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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.

Additional file 1: Figure S1. Dry cell weights of SIPI12-34, ΔTetR1 and ΔTetR1/TetR1 at different times during the fermentation stage. Figure S2. The elimination of genomic DNA in obtained RNA of SIPI12-34 and ΔTetR1 were confirmed by PCR amplification using the primer pairs 16S1-F/R. Figure S3. The elimination of genomic DNA in obtained RNA of SIPI12-34 and SIPI12-34/TetR1 were confirmed by PCR amplification using the primer pairs 16S1-F/R. Figure S4. Acarbose production profiles of SIPI12-34 and SIPI12-34/dCas9-EMP were analyzed by HPLC. Figure S5. Dry cell weights of SIPI12-34 and SIPI12-34/dCas9-EMP at different times during the fermentation stage. Figure S6. The expression of genes encoding glycogen synthase derived from transcriptome sequencing. Figure S7. PCR validation of ΔGS and SIPI12-34. Figure S8. Dry cell weights of SIPI12-34 and ΔGS/glgP at different times during the fermentation stage. Figure S9. Dry cell weights of SIPI12-34 and SIPI2207 at different times during the fermentation stage. Figure S10. Dry cell weights of SIPI2207 with addition of different sugars during the fermentation stage. Figure S11. Acarbose production profiles of SIPI12-34 and SIPI12-34/acbD were analyzed by HPLC.

Dihydromyricetin (DHM) is a flavonoid extracted from the leaves and stems of the edible plant Ampelopsis grossedentata that has been used for Chinese Traditional Medicine. It has attracted considerable attention from consumers due to its beneficial properties including anticancer, antioxidative, and anti-inflammatory activities. Continuous oxidative stress caused by intracellular redox imbalance can lead to chronic inflammation, which is intimately associated with the initiation, promotion, and progression of cancer. DHM is considered a potential redox regulator for chronic disease prevention, and its biological activities are abundantly evaluated by using diverse cell and animal models. However, clinical investigations are still scanty. This review summarizes the current potential chemopreventive effects of DHM, including its properties such as anticancer, antioxidative, and anti-inflammatory activities, and further discusses the underlying molecular mechanisms of DHM in cancer chemoprevention by targeting redox balance and influencing the gut microbiota.