This　study　investigates　the　correlation　between　various　micromagnetic　signature　patterns　and　the　yield　and　tensile　strengths　of　carbon　steel　(Cr12MoV　steel　as　per　Chinese　standards).　For　this　purpose,　back-propagation　neural　network　(BP-NN)　models　are　established　to　quantitatively　predict　the　yield　and　tensile　strengths　of　carbon　steels.　The　accuracy　of　prediction　models　is　significantly　affected　by　the　presence　of　redundant　micromagnetic　signature　patterns.　By　carefully　screening　the　input　parameters,　it　is　able　to　effectively　mitigate　prediction　errors　arising　from　unreasonable　model　inputs.　In　the　field　of　micromagnetic　nondestructive　testing　(NDT),　prediction　models　calibrated　for　a　specific　instrument　or　sensor　cannot　be　directly　applied　to　another　instrument　or　sensor.　In　the　study,　a　joint　distribution　adaptation　transfer　learning　strategy　based　on　auxiliary　data　is　proposed　to　enhance　the　generalization　of　prediction　models　for　cross-instrument　applications.　When　auxiliary　data　accounts　for　30%　of　the　source　domain　data,　the　joint　distribution　adaptation　transfer　learning　method　based　on　auxiliary　data　improves　the　robustness　of　the　model.　The　accuracy　of　the　yield　strength　and　tensile　strength　calibration　models　witnesses　remarkable　improvements　of　approximately　91.4%　and　93.5%,　respectively.