# Machine learning for automated quality control in injection moulding manufacturing

arXiv: https://arxiv.org/abs/2206.15285 · HTML: https://mlsanity.com/paper/2206.15285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Quality Prediction in Injection Molding](https://mlsanity.com/paper/1804.04899/) (2018)

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

- [Intelligent Vacuum Thermoforming Process](https://mlsanity.com/paper/2509.13250/) (2025)

## Abstract (arXiv)

Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was 99.4%, 99.7% and 94.7%, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.