Because of the time necessary for performing experiments on high dimensionality design space in manufacturing processes, exploring the entire design space is often prohibitively costly and time-consuming. To accelerate the development of NiTiHf shape memory alloys, we have proposed a sequential learning design framework to strategically identify experimental candidates who maximize information gain using fewest possible experiments. A Kriging regression model scales to high-dimensional search space in terms of processing features, part properties and alloy...
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