Effects of trace addition of Ag on the fatigue crack propagation behavior and microstructure of a mediumstrength aged Al–Zn–Mg alloy were investigated in the present work. The results show that a combination of enhanced tensile strength and improved fatigue crack propagation resistance in Al–Zn–Mg alloys is achieved with small addition of Ag. The enhanced strength is attributed to the high density of η’ precipitates within the grains and narrow precipitate free zones in the vicinity of grain boundaries. The main contribution to the improvement of fatigue crack propagation resistance comes from the coarser precipitates within the grains. When subjected to two-step aging, Ag-added alloy shows larger semi-coherent matrix precipitates. These relatively coarser precipitates increase the homogeneity of deformation and therefore improve the fatigue crack propagation resistance. In addition, microstructure analysis indicates that the size and distribution of inclusions as well as the grain structures of Al–Zn–Mg alloys are independent of Ag addition.
The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer.The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods.In order to overcome the shortcomings of the current methods,the Ω phase in Al-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the Al alloy TEM(transmission electron microscope)digital images process and recognize and extract the information of the second phase in the result image automatically.The top-hat transformation of the mathematical morphology,as well as several imaging processing technologies has been used in the proposed algorithm.Thereinto,top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image.The testing results are satisfied,which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method.The omission of these two kinds of Ω phase can be avoided or significantly reduced.More Ω phases would be recognized(growing rate minimum to 2% and maximum to 400% in samples),accuracy of recognition and statistics results would be greatly improved by using this method.And the manual error can be eliminated.The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software.It can process one image,including recognition and quantitative analysis in 30 min,but the manual method such as using Image Tool or Nano Measurer need 2 h or more.The labor intensity is effectively reduced and the working efficiency is greatly improved.
