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null (Ed.)Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM.more » « less
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Abstract Aim Soil‐borne pathogens severely affect crop production, but the present distribution of agricultural soil‐borne pathogens and their response to global changes are unexplored at large spatial scales. Here, we examine the nationwide‐scale distribution patterns, dominant taxa and environmental drivers of fungal soil‐borne pathogens, and their response to warming, nutrient enrichment and their interaction.
Location China.
Time period July and August 2019.
Major taxa studied Fungal plant pathogens.
Methods Through nationwide field surveys of 711 top‐ and subsoil samples in 51 cropland locations, we investigated the distribution patterns, environmental drivers and dominant taxa of fungal plant pathogens. We then conducted a mesocosm experiment with soils collected at 40 survey locations to evaluate the response patterns of fungal pathogens to global changes, including warming, nutrient enrichment and their interaction.
Results We observed that the abundance and richness of potential soil‐borne pathogens were higher in the topsoil than in the subsoil. Mean annual temperature and mean annual precipitation as the main drivers had a stronger effect on the abundance, richness and community of pathogens in the topsoil than subsoil. Two phylotypes, belonging to genus
Fusarium , were the dominant soil‐borne pathogens accounting for approximately one third of total abundance, and their abundances (e.g. relative and absolute abundance via quantitative polymerase chain reaction) were negatively correlated with precipitation and temperature. The mesocosm experiment simulating global changes further revealed that the abundance and richness distributions of soil pathogens predicted the direction of their response to global changes, with a positive response in pathogen‐poor soil and negative in pathogen‐rich soil. We further constructed spatial atlases of the dominant soil‐borne pathogens and their responses to global changes in agricultural fields.Main conclusions Our findings suggest that the current distribution of potential soil‐borne pathogens is regulated by climate, which could affect their future dynamics and is vital to agricultural practices for pathogen control and crop production.
