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In recent years, manufacturing industries (e.g., medical, aerospace, and automobile) have been changing their manufacturing process to small-quantity batch production to flexibly cope with fluctuations in demand. Therefore, many companies are trying to produce products by introducing 3D printing technology into the manufacturing process. The 3D printing process is based on additive manufacturing (AM), which can fabricate complex shapes and reduce material waste and production time. Although AM has many advantages, its product quality is poor compared to conventional manufacturing systems. This study proposes a methodology to improve the quality of AM products based on data analysis. The targeted quality of AM is the surface roughness of the stacked wall. Surface roughness is one of the important quality indicators and can cause short product life and poor structure performance. To control the surface roughness, the resultant surface roughness needs to be predicted in advance depending on the process parameters. Various analysis methods such as data pre-processing and deep neural networks (DNN) combined with sensor data are used to predict surface roughness in the proposed methodology. The proposed methodology is applied to field data from operated wire + arc additive manufacturing (WAAM), and the analysis result shows its effectiveness, with a mean absolute percentage error (MAPE) of 1.93%. 

Herein, the feasibility of the gas tungsten arc welding‐based wire + arc additive manufacturing process for fabricating thin wall structures of niobium‐1 wt% zirconium (NbZr1) alloy is investigated. Three different heat input conditions (low, medium, and high) have been selected for fabricating it. The microstructure is characterized by using optical microscopy, scanning electron microscopy, X‐ray diffraction, energy‐dispersive spectroscopy, and electron backscattered diffraction (EBSD). The microstructure shows the columnar dendritic structure elongated in the build direction. No cracks or porosity are observed in the structure. Average Vickers hardness for low, medium, and high heat input conditions are 146.6, 162.1, and 163.5 HV, respectively. There is an increasing trend of microhardness value along the deposition height, which can be attributed to the difference in secondary dendritic arm spacing and the formation of precipitates. The tensile strength of the specimen is comparable to the conventional and additively manufactured structures. EBSD analysis confirms that possible subgrains are responsible for good mechanical properties at room temperature. In the majority of the tensile samples, the failure mechanism has been identified as a ductile fracture. The mechanical characteristics fluctuate with locations in each of the thin walls, suggesting anisotropy in the deposits.