Robust　and　quantitative　prediction　of　surface　hardened　layer　depth　of　45　steel　using　micromagnetic　testing　method　was　studied　considering　the　repeatability　of　multi-functional　micro-magnetic　instrument　in　measuring　multiple　magnetic　features.　First,　the　repeatability　of　instrument　in　measuring　41　magnetic　features　was　evaluated　based　on　the　statistical　method　of　coefficient　of　variation　(β)　of　the　test　data.　By　combining　the　index　of　β　and　the　sensitivity　(S)　of　magnetic　feature　to　the　variation　in　hardened　layer　depth,　the　magnetic　features　were　filtered.　Second,　models　of　feedforward　neural　network　(FNN)　were　established　fusing　multiple　micro-magnetic　features.　Modeling　strategy　for　improving　the　robustness　of　the　model　and　model　robustness　evaluation　method　were　proposed.　Finally,　the　effect　of　rules　of　input　nodes　elimination　and　reservation　on　the　robustness　of　the　model　were　discussed.　Compared　with　the　traditional　modeling　method,　when　eight　magneric　features　were　eliminated　from　the　input　nodes　of　FNN　model　obeying　the　proposed　rule,　the　mean　value　of　MAE　and　the　number　of　models　with　MAE　value　less　than　5%　decreased　by　about　68.　8%　and　increased　by　150%,　respectively.　This　indicates　that　the　proposed　modeling　strategy　can　effectively　improve　the　robustness　of　the　instrument,　which　is　used　for　quantitative　prediction　of　the　surface　hardened　layer　depth　of　45　steel.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.