PurposeCurrent　flexible　printed　circuit　(FPC)　assembly　relies　heavily　on　manual　labor,　limiting　capacity　and　increasing　costs.　Small　FPC　size　makes　automation　challenging　as　terminals　can　be　visually　occluded.　The　purpose　of　this　study　is　to　use　3D　tactile　sensing　to　mimic　human　manual　mating　skills　for　enabling　sensing　offset　between　FPC　terminals　(FPC-t)　and　FPC　mating　slots　(FPC-s)　under　visual　occlusion.Design/methodology/approachThe　proposed　model　has　three　stages:　spatial　encoding,　offset　estimation　and　action　strategy.　The　spatial　encoder　maps　sparse　3D　tactile　data　into　a　compact　1D　feature　capturing　valid　spatial　assembly　information　to　enable　temporal　processing.　To　compensate　for　low　sensor　resolution,　consecutive　spatial　features　are　input　to　a　multistage　temporal　convolutional　network　which　estimates　alignment　offsets.　The　robot　then　performs　alignment　or　mating　actions　based　on　the　estimated　offsets.FindingsExperiments　are　conducted　on　a　Redmi　Note　4　smartphone　assembly　platform.　Compared　to　other　models,　the　proposed　approach　achieves　superior　offset　estimation.　Within　limited　trials,　it　successfully　assembles　FPCs　under　visual　occlusion　using　three-axis　tactile　sensing.Originality/valueA　spatial　encoder　is　designed　to　encode　three-axis　tactile　data　into　feature　maps,　overcoming　multistage　temporal　convolution　network＇s　(MS-TCN)　inability　to　directly　process　such　input.　Modifying　the　output　to　estimate　assembly　offsets　with　related　motion　semantics　overcame　MS-TCN＇s　segmentation　points　output,　unable　to　meet　assembly　monitoring　needs.　Training　and　testing　the　improved　MS-TCN　on　an　FPC　data　set　demonstrated　accurate　monitoring　of　the　full　process.　An　assembly　platform　verified　performance　on　automated　FPC　assembly.