Towards Model-Based Assessment of Trustworthiness in Autonomous Cyber-Physical Production Systems

The latest industrial revolution has introduced the concept of autonomous cyber-physical production systems by integrating machine learning components into smart manufacturing systems. Although it does help maximize the production process while efficiently managing the required resources, integrating machine learning into those systems has a major impact on trustworthiness due to less predictable and explainable behaviors.This paper proposes an overview of a novel model-based methodology for evaluating the trustworthiness of those systems. In order to develop the methodology, we conducted a study to understand the potential and limitations of model-based assessment and categorized limitations into structural, behavioral, and resource-related. According to those findings, we concluded that machine learning components within autonomous cyber-physical production systems are not adequately considered regarding risk identification and assessment. Moreover, the study revealed that using a single modeling approach can limit the evaluation process to specific layers or attributes. Therefore, based on the conclusion drawn from this study, we propose a new methodology to overcome current limitations in identifying and assessing risks originating from machine learning components within autonomous cyber-physical production systems.