# Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods

arXiv: https://arxiv.org/abs/2403.18731 · HTML: https://mlsanity.com/paper/2403.18731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization](https://mlsanity.com/paper/2010.14824/) (2020)
- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Utilizing Explainable AI for improving the Performance of Neural Networks](https://mlsanity.com/paper/2210.04686/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient Milling Quality Prediction with Explainable Machine Learning](https://mlsanity.com/paper/2409.10203/) (2024)

## Abstract (arXiv)

This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features identified through explainability methods are eliminated. This procedural refinement results in performance enhancements, paving the way for potential reductions in manufacturing costs and a better understanding of the trained ML models. This study highlights the usefulness of explainability techniques in both explaining and optimizing predictive models in the manufacturing realm.