  Design exploration is an important step in the engineering design process.
This involves the search for design/s that meet the specified design criteria
and accomplishes the predefined objective/s. In recent years, machine
learning-based approaches have been widely used in engineering design problems.
This paper showcases Artificial Neural Network (ANN) architecture applied to an
engineering design problem to explore and identify improved design solutions.
The case problem of this study is the design of flexible disc elements used in
disc couplings. We are required to improve the design of the disc elements by
lowering the mass and stress without lowering the torque transmission and
misalignment capability. To accomplish this objective, we employ ANN coupled
with genetic algorithm in the design exploration step to identify designs that
meet the specified criteria (torque and misalignment) while having minimum mass
and stress. The results are comparable to the optimized results obtained from
the traditional response surface method. This can have huge advantage when we
are evaluating conceptual designs against multiple conflicting requirements.

Neural and Evolutionary Computing;Machine Learning;Machine Learning