  The braking performance of the brake system is a target performance that must
be considered for vehicle development. Apparent piston travel (APT) and drag
torque are the most representative factors for evaluating braking performance.
In particular, as the two performance factors have a conflicting relationship
with each other, a multidisciplinary design optimization (MDO) approach is
required for brake design. However, the computational cost of MDO increases as
the number of disciplines increases. Recent studies on inverse design that use
deep learning (DL) have established the possibility of instantly generating an
optimal design that can satisfy the target performance without implementing an
iterative optimization process. This study proposes a DL-based
multidisciplinary inverse design (MID) that simultaneously satisfies multiple
targets, such as the APT and drag torque of the brake system. Results show that
the proposed inverse design can find the optimal design more efficiently
compared with the conventional optimization methods, such as backpropagation
and sequential quadratic programming. The MID achieved a similar performance to
the single-disciplinary inverse design in terms of accuracy and computational
cost. A novel design was derived on the basis of results, and the same
performance was satisfied as that of the existing design.
