Introducing a procedural knowledge model for enhancing industrial process adaptiveness
Time: Fri 2021-09-17 10.00
Location: https://kth-se.zoom.us/j/63998476971, Stockholm (English)
Subject area: Production Engineering
Doctoral student: Dr Eng. Andrea de Giorgio , Industriell produktion
Opponent: Gino Dini, Università di Pisa, Dipartimento di Ingegneria Civile e Industriale (DICI)
Supervisor: Professor Lihui Wang, Hållbara produktionssystem; Professor Mauro Onori, Industriell produktion
Industrial processes are mainly based on procedural knowledge that must be continually elicited from experienced operators and learned by novice operators. In the context of Industry 4.0, machines already play a key role in knowledge transfer; however, new models and methods based on the artificial intelligence advances of the past few years need to be developed and applied. The future of human-machine collaboration is not limited to physical applications, but it has the potential to harness both the strength of human skills, experience and the computational power provided by the surrounding machines for truly adaptive industrial processes. The winning recipe is a balance between letting humans exploit their inherent experience and letting machines integrate the missing skills to preserve production standards. This work introduces a procedural knowledge model to be used for the design of industrial and scientific adaptive processes and it paves the way to transforming human-machine collaboration into an efficient solution to make industrial and scientific processes resilient to a constantly changing world.