During　the　butt-welding　process　of　thin-plate　aluminum　alloys,　the　base　metal　was　affected　by　fluctuations　in　the　joint　gap　and　uneven　heating　and　heat　dissipation.　As　a　result,　the　weld　was　usually　defective.　A　passive　vision　sensing　method　was　selected　to　capture　the　front-side　welding　images　which　contained　dynamic　changes　in　the　molten　pool.　A　weld　formation　image　database　for　the　thin-plate　aluminum　alloy　with　a　thickness　of　3　mm　using　butt　tungsten　inert　gas　(TIG)　was　established.　A　double-layer　tandem　weld　formation　prediction　network　model　was　proposed　to　predict　the　weld　formation　under　conditions　such　as　incomplete　penetration,　normal　penetration,　over-penetration,　burn-through,　left　misalignment,　and　right　misalignment.　The　first-layer　weld　formation　prediction　network　predicted　three　types　of　irregular　weld　formations,　such　as　burn-through,　left　misalignment,　and　right　misalignment,　as　well　as　normal　weld　formation.　The　second-layer　weld　penetration　prediction　network　further　classified　the　images　of　normal　weld　formations　into　incomplete　penetration,　normal　penetration,　and　over-penetration.　Different　datasets　were　used　to　train　the　model,　respectively.　The　image-enhanced　dataset　showed　the　most　excellent　performance,　and　the　overall　prediction　accuracy　of　the　model　could　reach　95%.　Welding　tests　with　varying　joint　gap　fluctuations,　heat　dissipation,　and　misalignments　were　carried　out,　verifying　that　the　model　could　accurately　classify　six　types　of　different　weld　formations　in　image　sequences.　©　2025　Harbin　Research　Institute　of　Welding.　All　rights　reserved.