STARdom: an architecture for trusted and secure human-centered manufacturing systems

TL;DR

This paper proposes STARdom, a comprehensive architecture for trusted, secure, and human-centered manufacturing systems that integrates AI, explainability, feedback, and active learning to enhance decision-making in Industry 5.0.

Contribution

It introduces a unified architecture tailored for human-centered manufacturing, integrating forecasts, explainability, feedback, and security, aligned with industry standards.

Findings

Validated on a real-world demand forecasting case study

Enhanced forecast accuracy through active learning and simulated reality

Addresses security concerns in human-centered AI manufacturing systems

Abstract

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback, and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.