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Linköping 2018Cover image: A fracture surface of EBM IN718 after tensile test at room temperature.During the course of research underlying this thesis, Dunyong Deng was enrolled in Agora Materiae, a multidiciplinary doctoral program at Linköping University, Sweden. Printed by LiU-Tryck 2018 © Dunyong Deng AbstractAdditive manufacturing (AM), also known as 3D printing, has gained significant interest in aerospace, energy, automotive and medical industries due to its capabilities of manufacturing components that are either prohibitively costly or impossible to manufacture by conventional processes. Among the various additive manufacturing processes for metallic components, electron beam melting (EBM) and selective laser melting (SLM) are two of the most widely used powder bed based processes, and have shown great potential for manufacturing high-end critical components, such as turbine blades and customized medical implants. The futures of the EBM and SLM are doubtlessly promising, but to fully realize their potentials there are still many challenges to overcome.Inconel 718 (IN718) is a nickel-base superalloy and has impressive combination of good mechanical properties and low cost. Though IN718 is being mostly used as a turbine disk material now, the initial introduction of IN718 was to overcome the poor weldability of superalloys in 1960s, since sluggish precipitation of strengthening phases γ ′ /γ ′′ enables good resistance to strain-age cracking during welding or post weld heat treatment. Given the similarity between AM and welding processes, IN718 has been widely applied to the metallic AM field to facilitate the understandings of process-microstructure-property relationships.The work presented in this licentiate thesis aims to better understand microstructures and mechanical properties EBM and SLM IN718, which have not been systematically investigated. Microstructures of EBM and SLM IN718 have been characterized with scanning electron microscopy (SEM), transmission electron microscopy (TEM) and correlated with the process conditions. Monotonic mechanical properties (e.g., Vickers microhardness and tensile properties) have also been measured and rationalized with regards to the microstructure evolutions before and after heat treatments.For EBM IN718, the results show the microstructure is not homogeneous but dependant on the location in the components, and the anisotropic mechanical properties are probably attributed to alignment of porosities rather than texture.iii Post heat treatment can slightly increase the mechanical strength compared to the as-manufactured condition but does not alter the anisotropy. SLM IN718 shows significantly different microstructure and mechanical properties to EBM IN718. The as-manufactured SLM IN718 has very fine dendritic microstructure and Laves phases in the interdendrites, and is "work-hardened" by the residual strains and dislocations present in the material. Mechanical properties are different between horizontally and vertically built samples, and heat treatment can m...

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Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer's high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R 2 , 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.

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