  Studies on manufacturing cost prediction based on deep learning have begun in
recent years, but the cost prediction rationale cannot be explained because the
models are still used as a black box. This study aims to propose a
manufacturing cost prediction process for 3D computer-aided design (CAD) models
using explainable artificial intelligence. The proposed process can visualize
the machining features of the 3D CAD model that are influencing the increase in
manufacturing costs. The proposed process consists of (1) data collection and
pre-processing, (2) 3D deep learning architecture exploration, and (3)
visualization to explain the prediction results. The proposed deep learning
model shows high predictability of manufacturing cost for the computer
numerical control (CNC) machined parts. In particular, using 3D
gradient-weighted class activation mapping proves that the proposed model not
only can detect the CNC machining features but also can differentiate the
machining difficulty for the same feature. Using the proposed process, we can
provide a design guidance to engineering designers in reducing manufacturing
costs during the conceptual design phase. We can also provide real-time
quotations and redesign proposals to online manufacturing platform customers.
