# Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

arXiv: https://arxiv.org/abs/2202.02003 · HTML: https://mlsanity.com/paper/2202.02003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Data-driven polynomial chaos expansion for machine learning regression](https://mlsanity.com/paper/1808.03216/) (2018)
- [Hard Shape-Constrained Kernel Machines](https://mlsanity.com/paper/2005.12636/) (2020)
- [Bayesian Monotone Regression using Gaussian Process Projection](https://mlsanity.com/paper/1306.4041/) (2013)
- [Approximation, characterization, and continuity of multivariate monotonic regression functions](https://mlsanity.com/paper/2009.02317/) (2020)
- [Compensating data shortages in manufacturing with monotonicity knowledge](https://mlsanity.com/paper/2010.15955/) (2020)
- [Approximate solutions of convex semi-infinite optimization problems in finitely many iterations](https://mlsanity.com/paper/2105.08417/) (2021)

## Built on by (most cited first, 3 of 3)

- [Towards Automated Knowledge Integration From Human-Interpretable Representations](https://mlsanity.com/paper/2402.16105/) (2024)
- [IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industry](https://mlsanity.com/paper/2507.15268/) (2025)
- [Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles](https://mlsanity.com/paper/2608.05761/) (2026)

## Abstract (arXiv)

Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such machine learning models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with machine learning methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input-output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional machine learning methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. Another merit of the proposed methodology is that, in contrast to most conventional machine learning methods, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.