To address the consistency issues in the ceramic additive manufacturing process, a solution is proposed involving the use of computer vision to acquire monitoring images of the manufacturing surface. The obtained image data is then input into the YOLOv3 model for training, aiming to detect and eliminate defects during the manufacturing process. To tackle the problem of uneven illumination at different positions on the large-scale manufacturing surface, a method using homomorphic filtering is proposed to remove the illumination components from the images. The parameters of the Gaussian-type homomorphic filter are determined through theoretical analysis and experimental validation. The results demonstrate that after homomorphic filtering, the detection rate for bubble defects increased by 6.7% and the detection rate for pit defects increased by 2.3%.
Large-scale ceramic additive manufacturing technology has the advantages of large manufacturing size and high manufacturing accuracy. It is an advanced manufacturing technology with relatively rapid development and can be widely used in aerospace, chemical, energy, and other applications. It is necessary to analyze the types and causes of its forming errors to give full play to its advantages in high-precision manufacturing. This paper took the manufacturing equipment of large-size ceramic additives as the research object, introduced its technical characteristics and manufacturing process, and analyzed the causes and effects of the errors generated in each manufacturing step. The research results can provide the theoretical basis for error compensation research.
Additive manufacturing utilizes a layer-by-layer stacking method to process parts, which simplifies the manufacturing process. But the staircase effect has a great impact on the accuracy of surfaces, so it is necessary to select some reasonable building orientations considering different types of features and tolerance requirements. This paper uses the Open CASCADE Technology to identify the tolerance, topological shape, and geometric object in the STEP AP 242 file format and selects a reasonable building orientation set for required model sub-features according to the geometric characteristics of different kinds of topological shapes. The precision value of each building orientation in the set is calculated by tolerance weighting, and the value ranking of each orientation for ensuring surface accuracy is obtained, which can be used as a reference for the subsequent determination of the building orientation. Experiments on 10 models are conducted to verify the effectiveness and rapidity of the proposed method.
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