Procedural knowledge and function blocks for smart process planning

In the age of digital manufacturing there is a need to elicit and transfer procedural knowledge between humans and machines. Having proper knowledge is essential in decision-making. The more the knowledge, the better decisions are made. To capture experiences and turn them into knowledge is fundamental in learning processes and knowledge development. Knowledge engineering and knowledge management have been subject for research for decades and several concepts about knowledge and knowledge transfer have been introduced, but a functional approach to exploit knowledge efficiently in manufacturing is still missing. In the era of Industry 4.0, humans and machines must be able to collaborate in such way that both can exploit the best abilities of each other in a manufacturing process. This paper introduces a procedural knowledge process (PKP) approach to capturing and defining unexpected events, while a process step is able to perform its required functions and transfer that information as machine-understandable knowledge about a failure mode. Function blocks (FBs), as per the IEC-61499 standard, have been proposed as a way to achieve distributed process planning in which the manufacturing process can adapt itself to runtime conditions, e.g. machine availability, etc. However, FBs are event-driven systems and the approach is limited to work under well-known runtime conditions, e.g. machine configurations and states, or deviations which are impossible to foresee in advance, for instance the outcome of a process failure mode effects analysis (PFMEA). The PKP introduced in this paper, aims at bridging this gap by integrating at runtime an expert operator’s solution based on root cause analysis (RCA) in an FB architecture, making the FB knowledge-driven systems, for further executions of the same without redesigning it. Natural language representations of procedural knowledge blocks (PKBs) allow to transfer procedural knowledge to human operators, i.e. explain the process flow of a machine decision, while machine representations of PKBs allow to embed procedural knowledge that is elicited from expert operators upon unexpected events into the FBs process. The resulting PKP enhances the FBs for smart industrial applications, such as the process planning use case described in this paper.