To　obtain　the　grasping　sequence　of　the　robotic　arm　for　grasping　target　objects　in　a　stacked　workpiece　industrial　scenario,　this　paper　proposes　an　instance　segmentation　and　grasping　sequence　method　for　stacked　workpieces.　Firstly,　a　Mask　R-CNN　instance　segmentation　network　based　on　boundary　refinement　is　designed　to　obtain　more　accurate　instance　mask　information.　Additionally,　a　Euclidean　clustering　method　based　on　smoothness　parameters　is　integrated　to　correct　extreme　cases　of　mis-segmentation　by　the　improved　Mask　R-CNN　instance　segmentation　network　from　the　perspective　of　three-dimensional　space　(where　adjacent　closely　stacked　workpieces　are　mis-segmented　as　the　same　workpiece).　Secondly,　a　stacking　workpiece　grasping　sequence　filtering　mechanism　is　proposed.　This　mechanism　determines　the　occlusion　status　of　the　target　workpiece　to　be　grasped　by　judging　the　relationship　between　visible　masks　and　complete　masks　obtained　through　instance　segmentation.　Furthermore,　based　on　the　grasping　judgment　scores,　the　workpieces　in　unoccluded　states　are　arranged　in　sequence,　filtering　out　target　workpieces　that　satisfy　the　principle　of　grasping　unoccluded　state　workpieces　first,　in　a　top-to-bottom　manner.　Experimental　results　demonstrate　that　the　proposed　method　can　effectively　infer　the　grasping　sequence　in　stacked　workpiece　environments,　thereby　driving　the　robotic　arm　to　perform　grasping　tasks.　©　2024　IEEE.