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.

基于深度学习的制造成本预测研究近年来已经开始，但由于模型仍被用作黑匣子，因此无法解释成本预测的基本原理。本研究旨在提出一种使用可解释人工智能的 3D 计算机辅助设计 (CAD) 模型的制造成本预测过程。所提出的过程可以可视化影响制造成本增加的 3D CAD 模型的加工特征。所提出的过程包括（1）数据收集和预处理，（2）3D深度学习架构探索，以及（3）可视化以解释预测结果。所提出的深度学习模型显示了计算机数控 (CNC) 加工零件的制造成本的高度可预测性。特别是，使用3D梯度加权类激活映射证明所提出的模型不仅可以检测CNC加工特征，而且可以区分相同特征的加工难度。使用提议的过程，我们可以为工程设计师提供设计指导，以在概念设计阶段降低制造成本。我们还可以为在线制造平台客户提供实时报价和重新设计建议。