Robotic　friction　stir　welding　has　become　an　important　research　direction　in　friction　stir　welding　technology.　However,　the　low　stiffness　of　serial　industrial　robots　leads　to　substantial,　difficult-to-measure　end-effector　deviations　under　the　welding　forces　during　the　friction　stir　welding　process,　impacting　the　welding　quality.　To　more　effectively　measure　the　deviations　in　the　end-effector,　this　study　introduces　a　digital　twin　model　based　on　the　five-dimensional　digital　twin　theory.　The　model　obtains　the　current　data　of　the　robot　and　six-axis　force　sensor　and　calculates　the　real-time　end　deviations　using　the　robot　model.　Based　on　this,　a　virtual　welding　model　was　realized　by　integrating　the　FEA　model　with　the　digital　twin　model　using　a　co-simulation　approach.　This　model　achieves　pre-process　simulation　by　iteratively　cycling　through　the　simulated　force　from　the　FEA　model　and　the　end　displacement　from　the　robot　model.　The　virtual　welding　model　effectively　predicts　the　welding　outcomes　with　a　mere　6.9%　error　in　lateral　deviation　compared　to　actual　welding,　demonstrating　its　potential　in　optimizing　welding　parameters　and　enhancing　accuracy　and　quality.　Employing　digital　twin　models　to　monitor,　simulate,　and　optimize　the　welding　process　can　reduce　risks,　save　costs,　and　improve　efficiency,　providing　new　perspectives　for　optimizing　robotic　friction　stir　welding　processes.　©　2024　by　the　authors.