Progress On The Correlation Between Inclusions and Fatigue Behavior In NiTi Shape Memory Alloys For Biomedical Applications: Refinement Of The Statistical Approach

Thursday, May 15, 2014: 1:00 PM
Chapel (Asilomar Conference Grounds)
Dr. Alberto Coda , SAES Getters S.p.A, Lainate, Italy
Mr. Marco Urbano , SAES Getters S.p.A, Lainate, Italy
Dr. Andrea Cadelli , SAES Getters S.p.A, Lainate, Italy
Mr. Dennis W. Norwich , Memry Corporation, Bethel, CT
Mr. Frank Sczerzenie , SAES Smart Materials, New Hartford, NY
Dr. Pietro Luccarelli , Politecnico of Milano, Milano, Italy
Prof. Stefano Beretta , Politecnico of Milano, Milano, Italy
Quite recently, ASTM E2283-08 for statistical analysis of nonmetallic inclusions in steels has been introduced. Aim of the practice is to estimate the expected largest inclusion in a determined volume of material. The need of a thorough method for the analysis of inclusions followed the studies reported by Murakami, which demonstrated the negative effect of inclusions on the fatigue behavior of hard steels and the need to adopt the extreme value inclusion rating.

In this work a similar approach is considered for NiTi SMAs. A comprehensive experimental campaign has been carried out on materials coming from five different melting and hot processing routes. Surface finish, cold working, and final straight annealing have been carried out according to a standard recipe. Results concerning rotary bending fatigue on 0.3 mm superelastic wires are presented. By means of a simple interpretative model, called bilinear scatter band, the fatigue performances of the five material classes have been well described and compared, and results confirm that they are different. The fracture surfaces of failed wires have been analyzed through FESEM microscopy and data regarding the presence of inclusions and their morphology have been recorded. Finally, the Type-III generalized extreme value (GEV) distribution of particles for the selected materials has been identified and its relationship with the fatigue limit has been clearly demonstrated. The extreme value statistics is confirmed to be a valuable approach for inclusions rating in SMAs for biomedical applications. An outline about on-going and future work will also be provided.

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