Gas　tungsten　arc　welding　(GTAW)　is　the　primary　joining　process　for　critical　applications　where　joining　precision　is　crucial.　However,　variations　in　manufacturing　conditions　adversely　affect　the　joining　precision.　The　dynamic　joining　process　needs　to　be　monitored　and　adaptively　controlled　to　assure　the　specified　weld　quality　be　produced　despite　variations.　Among　required　weld　qualities,　the　weld　joint　penetration　is　often　the　most　critical　one　as　an　incomplete　penetration　causes　explosion　under　high　temperature/pressure　and　an　excessive　penetration/heat　input　affects　the　flow　of　fluids　and　degrades　materials　properties.　Unfortunately,　detecting　its　development,　how　the　melted　metal　has　developed　within　the　work-piece,　is　challenging　as　it　occurs　underneath　and　is　not　directly　observable.　The　key　to　solving　the　problem　is　to　find,　or　design,　measurable　physical　phenomena　that　are　fully　determined　by　the　weld　penetration　and　then　correlate　the　phenomena　to　the　penetration.　Analysis　shows　that　the　weld　pool　surface　that　is　directly　observable　using　an　innovative　active　vision　method　developed　at　the　University　of　Kentucky　is　correlated　to　the　thermal　expansion　of　melted　metal,　thus　the　weld　penetration.　However,　the　surface　is　also　affected　by　prior　conditions.　As　such,　we　propose　to　form　a　composite　image　from　the　image　taken　from　the　initial　pool,　reflecting　prior　condition　and　from　real-time　developing　pool　such　that　this　single　composite　image　reflecting　the　measurable　phenomena　is　only　determined　by　the　development　of　the　weld　penetration.　To　further　correlate　the　measurable　phenomena　to　the　weld　penetration,　conventional　methods　analyze　the　date/images　and　propose　features　that　may　fundamentally　characterize　the　phenomena.　This　kind　of　hand　engineering　method　is　tedious　and　does　not　assure　success.　To　address　this　challenge,　a　convolutional　neural　network　(CNN)　is　adopted　that　allows　the　raw　composite　images　to　be　used　directly　as　the　input　without　need　for　hand　engineering　to　manually　analyze　the　features.　The　CNN　model　is　applied　to　train,　verify　and　test　the　datasets　and　the　generated　training　model　is　used　to　identify　the　penetration　states　such　that　the　welding　current　can　be　reduced　from　the　peak　to　the　base　level　after　the　desired　penetration　state　is　achieved　despite　manufacturing　condition　variations.　The　results　show　that　the　accuracy　of　the　CNN　model　is　approximately　97.5%.