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A method for recognition of casting defects based on improved You Only Look Once (YOLO v3) is proposed to address the problems of slow detection speed, low detection efficiency, and poor robustness suffered from the current inspecting manually methods, which can improve the ability to detect defects, especially for tiny defects. Firstly, for obtained the industrial digital radiography images (DR images), we introduce the guide filtering technique to enhance the defects in these DR images, thus obtaining standard defect samples; Further, the defect samples are annotated to generate the defect detection data set for network training. In this article, the improved YOLOv3 network model structure is used to detect defects. Comparative experiments illustrate the proposed defect detection model for castings achieves better performance. Concretely, the experimental results show that the improved network model (YOLOv3_134) converges faster than the YOLOv3 network model and has better convergence than the YOLOv3 model. And the mean average precision (mAP) of the YOLOv3_134 is 26.1% higher than that of the original YOLOv3, which makes the YOLOv3_134 model-based casting defect detection method meet the industrial production requirements in terms of accuracy and speed.

Aiming at the issue that mesh smoothing is hard to balance in terms of noise removing and feature preserving, in this article, we combine the facet normal representing global geometric features of the mesh with the vertex normal characterizing local details of the mesh and propose a bi-normal mesh smoothing method based on vertex feature selection (BNBVF). Firstly, the guided filtering algorithm is extended to calculate accurately the facet normal in the field of geometric processing. The key of this portion is computing the guided normal of facet by using a geometric neighboring patch with the most consistent normal. Then, an adaptive tensor voting method is employed to divide the vertices of the mesh into feature vertices and non-feature vertices. Thirdly, a method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate the normal of feature vertex, and the weighting average of first-order neighborhood facets of the vertex is applied to compute the normal of non-feature vertex. Finally, the vertices of the mesh are updated iteratively by combining the geometric information of the facet normal and vertex normal to achieve mesh smoothing. Experimental results demonstrate that the superior performance of our proposed algorithm to state-of-the-art approaches in feature preserving and error reducing.

Aiming at the difficulty of manufacturing error detection of internal cavity structural parts, a detection method of common manufacturing error based on industrial CT images was proposed. Firstly, the image sequence of part scanned by an industrial CT machine is converted into a three-dimensional measurement model; Then, the registration of the three-dimensional measurement model with the original design model is completed; The surface information of the part is obtained by segmenting surfaces of the three-dimensional measurement model; Next, the datum surface is selected, the error value of the test surface is calculated after selecting datum surface; Finally, the detection result is obtained by comparing the error value with the tolerance value, analyzing the result and the areas that do not meet the tolerance requirements is visualized in the developed software system. The common manufacturing errors of complex inner cavity parts can be detected by the method, such as dimension error of length, planeness error, cylindricity error, parallelism and perpendicularity error of face-to-face, at the same time, it can intuitively show the area whose manufacturing errors in the cavity structure of the parts are not satisfied, which provides a basis for judging the quality of manufacturing and processing of parts.

To make full use of parallel ability of multicore for ensuring the real-time system reliability constraints and low power consumption, a novel non-dependent directed acyclic graph algorithm was given based on coarse-grained software pipelining, which could transform a periodic dependency task graph into a set of independent tasks with retiming technique.