# Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization

arXiv: https://arxiv.org/abs/2010.14824 · HTML: https://mlsanity.com/paper/2010.14824/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress](https://mlsanity.com/paper/2210.01126/) (2022)
- [Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis](https://mlsanity.com/paper/2303.14581/) (2023)
- [VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation](https://mlsanity.com/paper/2406.12286/) (2024)
- [Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction](https://mlsanity.com/paper/2406.01996/) (2024)
- [Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods](https://mlsanity.com/paper/2201.12107/) (2022)
- [Concept Activation Vectors for Generating User-Defined 3D Shapes](https://mlsanity.com/paper/2205.02102/) (2022)
- [Machine Learning Meets Advanced Robotic Manipulation](https://mlsanity.com/paper/2309.12560/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending](https://mlsanity.com/paper/2603.13102/) (2026)
- [Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods](https://mlsanity.com/paper/2403.18731/) (2024)
- [CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts](https://mlsanity.com/paper/2605.12266/) (2026)
- [Efficient Milling Quality Prediction with Explainable Machine Learning](https://mlsanity.com/paper/2409.10203/) (2024)
- [Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling](https://mlsanity.com/paper/2504.20110/) (2025)
- [Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features](https://mlsanity.com/paper/2508.12440/) (2025)

## Abstract (arXiv)

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.