Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes

TL;DR

This paper introduces a deep learning-based inverse design method for automotive brakes that efficiently optimizes multiple performance targets simultaneously, reducing computational cost compared to traditional multidisciplinary design optimization methods.

Contribution

The study presents a novel deep learning-based multidisciplinary inverse design approach that achieves efficient, accurate optimization of brake system performance targets without iterative processes.

Findings

The proposed inverse design method outperforms conventional optimization in efficiency.

It achieves similar accuracy to single-disciplinary inverse design.

A new brake design meeting performance targets was successfully developed.

Abstract

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.