Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated.
|Journal||International journal of advanced manufacturing technology|
|Publication status||Published - 15 Dec 2019|
- Additive manufacturing
- Shapememory alloys
- Artificial neural network