Ball　screw　mechanisms　(BSMs)　are　used　as　accuracy　transmission　components　in　a　wide　range　of　industries　and　are　characterized　by　their　high　accuracy.　More　specifically,　the　positioning　accuracy　of　BSM　has　a　significant　effect　on　the　accuracy　of　machine　tool.　Based　on　the　macro-micro　multiscale　method,　an　exponential　prediction　model　for　the　BSM　positioning　accuracy　was　developed　considering　time-varying　working　conditions　(load　and　rotational　speed)　and　feed　modes.　Since　the　accuracy　degradation　is　mainly　caused　by　wear,　a　microscopic　approach　was　proposed　to　describe　the　positioning　accuracy　retention　and　the　microscopic　wear　process　was　investigated.　The　sliding　contact　of　the　asperities　between　the　ball　and　raceway　was　analyzed,　and　the　microscopic　wear　behavior　of　the　asperities　was　determined.　Considering　the　time-varying　working　conditions,　the　BSM　positioning　accuracy　characteristics　were　obtained　under　the　normal　feed　mode　by　conducting　suitable　tests.　The　exponential　wear　model　used　the　wear　index　to　describe　the　wear　status　based　on　the　positioning　accuracy　measurement.　The　accuracy　loss　value　and　the　prediction　index　of　positioning　accuracy　were　determined　based　on　an　exponential　model,　and　the　effective　lifetime　of　the　BSM　was　predicted.　Finally,　the　exponential　prediction　model　was　used　in　negative/positive　skew　feed　distribution,　and　the　effective　lifetime　determined.　©　IMechE　2020.