https://www.mdu.se/

An increase in consumer demand and scarcity of available resources has led industrialists to hunt for solutions related to the automation of traditional manufacturing and production processes, optimizing resource consumption while improving the overall efficiency of the process. The resultant revolution brought forward the concept of cyber-physical production systems. Furthermore, industries within the private sector have integrated artificial intelligence with their traditional production processes as Cobots (collaborative robots), thus introducing the concept of Autonomous Cyber-Physical Production Systems. Although these systems maximize the production or manufacturing process while efficiently using the available resources, the machine learning component integrated into the traditional cyber-physical production system brings about trust-related issues due to its possible lack of predictability and transparency. Implementing trust-related attributes within autonomous cyber-physical production systems alone cannot overcome the highlighted problem. Therefore, a detailed risk assessment is required to identify and assess any trust-related risks in the system, especially at the early stages of the software development life cycle, to avoid major incidents and reduce maintenance costs. Based on the above-stated facts, this research proposes a model-based risk assessment technique for evaluating the trustworthiness of autonomous cyber-physical production systems. The proposed technique focuses on the identification and assessment of trust-related risks originating from the dynamic behavior of the machine learning component in autonomous cyber-physical production systems. For this, we use existing standards and techniques proposed for risk assessment in cyber-physical production systems as common ground to facilitate better implementation of trustworthiness in autonomous cyber-physical production systems. The proposed technique is aimed at overcoming the structural and behavioral limitations reported in existing model-based risk assessment techniques when dealing with autonomous cyber-physical production systems.