In　order　to　accurately　predict　and　reduce　the　possible　defects　in　the　stamping　process　of　an　aluminum　alloy　sheet,　the　simulation　data　of　the　sheet　thickness　for　the　6016　aluminum　alloy　in　the　stamping　process　were　obtained　by　the　Hill＇48　yield　criterion　based　on　finite　element　ABAQUS/Explicit　solver.　Taking　blank　holder　force,　friction　coefficient,　stamping　speed,　and　die　clearance　as　input　parameters,　the　radial　basis　function　(RBF)　network　model　for　predicting　the　maximum　thinning　rate　of　the　stamping　aluminum　alloy　sheet　was　established.　The　results　show　that　the　RBF　network　model　constructed　in　this　paper　has　high　precision　and　can　reflect　the　complex　relationship　between　the　stamping　process　parameters　and　the　maximum　thinning　rate　well　by　comparing　the　finite　element　simulation　and　neural　network　prediction　results.　It　is　of　great　significance　to　improve　the　optimization　efficiency　of　the　stamping　process　of　the　aluminum　alloy　sheet　and　reduce　the　actual　experimental　cost.　©　Published　under　licence　by　IOP　Publishing　Ltd.