A method for generating a test coupon specification for predicting fatigue life of a component, comprising: (a) determining a load condition for said component, (b) providing a component design, (c) performing a strength analysis of the component design under said load condition determining: (i) a critical area of the component, (ii) at least one stress-related parameter of said critical area, (d) providing at least one material condition of said component at least for said critical area of the component, characterized by the additional steps: (e) providing a material model, (f) providing as an input to said material model: (i) said at least one stress-related parameter and (ii) said at least one material condition, (g) said material model generating as an output said test coupon specification for being tested in a testing machine, wherein said material model is a machine learning fatigue model, wherein said machine learning model is provided by a method comprising the steps of: (i) Providing a set of test coupon specifications to said material model, wherein said test coupon specifications respectively comprise different of said at least one material condition of said coupon specifications, (ii) collecting data points for maximum stress vs. cycles to failure for said test coupon specifications, (iii) training said material model with the collected data to select a test coupon specification from said set of test coupon specifications when receiving as an input: (i) said at least one stress-related parameter and (ii) said at least one material condition. (a) determining a load condition for said component, (b) providing a component design, (c) performing a strength analysis of the component design under said load condition determining: (i) a critical area of the component, (ii) at least one stress-related parameter of said critical area, (i) a critical area of the component, (ii) at least one stress-related parameter of said critical area, (d) providing at least one material condition of said component at least for said critical area of the component, (e) providing a material model, (f) providing as an input to said material model: (i) said at least one stress-related parameter and (ii) said at least one material condition, (i) said at least one stress-related parameter and (ii) said at least one material condition, (g) said material model generating as an output said test coupon specification for being tested in a testing machine, wherein said material model is a machine learning fatigue model, wherein said machine learning model is provided by a method comprising the steps of: (i) Providing a set of test coupon specifications to said material model, wherein said test coupon specifications respectively comprise different of said at least one material condition of said coupon specifications, (ii) collecting data points for maximum stress vs. cycles to failure for said test coupon specifications, (iii) training said material model with the collected data to select a test coupon specification from said set of test coupon specifications when receiving as an input: (i) said at least one stress-related parameter and (ii) said at least one material condition. (i) Providing a set of test coupon specifications to said material model, wherein said test coupon specifications respectively comprise different of said at least one material condition of said coupon specifications, (ii) collecting data points for maximum stress vs. cycles to failure for said test coupon specifications, (iii) training said material model with the collected data to select a test coupon specification from said set of test coupon specifications when receiving as an input: (i) said at least one stress-related parameter and (ii) said at least one material condition. (i) said at least one stress-related parameter and (ii) said at least one material condition.