The automatic and intelligent cutting process scheme for casting blank grinding was provided

and the segmentation and detection method of casting riser was investigated. FLA-Net

a point cloud network

was put forward based on the improvement of RandLA-NET

and an ACSE coding module combining cosine similarity was proposed to improve the segmentation accuracy of casting riser aiming at the issues that only the distance information is considered and the direction information is ignored in the network. In view of the disorder of point cloud

X space transformation matrix is used referring to the PointCNN segmentation framework

so that the network has space transformation invariance

and the network expression ability and semantic segmentation accuracy are improved. Finally

the experimental results demonstrate that the FLA-Net is more accurate and has a better prospect of engineering application compared with the classical point cloud semantic segmentation framework.