Towards a Testing Framework for Machine Learning Model Deployment in Manufacturing Systems

The deployment of machine learning models in manufacturing systems presents unique challenges, necessitating robust testing procedures to ensure reliable and efficient operation. This paper proposes an automated testing framework specifically designed to address these challenges, focusing on verifying the correct utilization of data sources, validating model functionality, and assessing the compatibility of the target machine with the deployed model. By automating the testing process, this framework aims to enhance the reliability and effectiveness of machine learning model deployment in manufacturing systems. Through a comprehensive literature review, the paper explores existing methodologies and identifies gaps in current practices. The proposed framework incorporates various test types, including unit tests, integration tests, regression tests, and performance tests, each tailored to the specific requirements of manufacturing systems. Experimental results demonstrate the framework’s effectiveness in detecting errors and failures during the deployment process. Overall, this research contributes to advancing the field of machine learning deployment in manufacturing systems and provides practical insights for practitioners seeking to optimize the reliability and efficiency of their deployed models.

Procedia CIRP, 127, 122-128