In　digital　light　processing　(DLP)　3D　printing,　fixed　control　protocol　parameters　limit　adaptability　and　lead　to　conservative　settings,　reducing　printing　efficiency.　Existing　methods　that　optimize　parameters　through　physical　models　linked　to　the　printing　process　do　not　account　for　real-time　environmental　changes,　often　resulting　in　potential　print　failures.　This　paper　proposes　a　dynamic　control　scheme　for　DLP　3D　printing　that　combines　physical　models　with　real-time　data.　Initially,　a　physical　model　of　the　printing　process　is　developed　to　pre-generate　the　printing　protocol.　Then,　a　multimodal　data　capture　scheme　is　designed　to　access　the　current　state　of　the　material　and　model.　Finally,　the　printing　protocol　parameters　are　dynamically　updated　based　on　the　analysis.　Experimental　results　demonstrate　that　this　method　optimizes　both　printing　time　and　success　rate,　significantly　enhancing　overall　printing　performance.　©　2024　Copyright　held　by　the　owner/author(s).