Machining　digital　twin　systems　represent　a　novel　platform　for　achieving　full　autonomy　in　machine　tools.　However,　in　multi-process　machining,　error　propagation　between　different　processes　presents　significant　challenges　to　current　digital　twin　systems:　real-time　model　updates,　error　evolution　analysis　and　optimization.　To　address　these　issues,　this　study　proposes　a　method　for　rapid　state　updates　of　shape　features　and　the　construction　of　an　error　propagation　knowledge　graph,　enabling　dynamic　control　in　a　multi-process　machining　digital　twin　system.　First,　a　state　update　method　based　on　multi-level　voxel　computation　is　introduced,　utilizing　the　transformation　relationship　between　cutter-workpiece　engagement　(CWE)　and　voxels　to　ensure　rapid　updates　of　multi-process　digital　twin　models.　Second,　digital　threads　for　multi-process　machining　are　developed　to　identify　machining　states　and　track　errors.　To　analyze　error　evolution　and　implement　effective　control　measures,　an　error　propagation　knowledge　graph　based　on　Taylor　expansion　model　is　constructed,　enabling　closed-loop　control.　Finally,　the　proposed　system　was　validated　for　its　advancement　in　error　control　and　closed-loop　optimization　through　the　machining　of　typical　multi-process　components　in　the　transportation　field.　The　results　of　comparative　experiments　and　ablation　experiments　indicate　that　the　error　updating　efficiency　based　on　multi-level　voxel　computation　threads　improved　by　an　average　of　59.26%　compared　to　other　benchmarks.　The　error　propagation　knowledge　graph　based　on　the　Taylor　model　achieved　a　42.75%　improvement　in　reasoning　accuracy　compared　to　the　ablated　Taylor　model.　The　developed　digital　twin　closed-loop　optimization　system　successfully　ensured　that　the　errors　in　1280　processes　of　the　case　study　remained　within　the　tolerance　range.