SIMMILAR: Systems-of-Systems for Intelligent Manufacturing Maintenance using Industry 4.0, Lean, AI Reasoning

 Industrial production is meeting new challenges in the global and digitalized economy where consumers are expecting almost instant delivery and where consumption patterns change rapidly based on quickly evolving trends. Those challenges include:

1. Increasing product variability requiring more flexible manufacturing solutions.

2. A need to look beyond single work stations or single factories to cross-organizational supply chains to reach further improvements of productivity and quality.

3. Faster turn-around time and 24/7 production, making less time available for maintenance and improvement of production equipment.

With this in mind, we believe that there is a need to better support maintenance engineers with analyses and work methods, combining Lean, basic maintenance management, and artificial intelligence (AI) using extensive data collected across the manufacturing SoS and represented in a digital twin of the production flow.

 This project should be seen as a pre-study, and has the following objectives:

1. Reach an understanding of suitable concepts based on SoS and AI in combination with basic maintenance management development for improving total maintenance effectiveness in flexible and distributed manufacturing.
2. Create a funding application for a larger research project involving more partners from industry and within XPRES to implement and validate the concepts that result from this initial study.
3. Strengthen collaboration within MDH through a joint project between ES and IPR applying computer science research on manufacturing problems.
4. Write and submit at least one joint research publication presenting the results of the project.
5. Provide input to courses at MDH (e.g. the FUTURe e-learning course on SoS engineering and the advanced course on Maintenance and dependability).

[Show all publications]

Analysis of Breakdown Reports using Natural Language Processing and Machine Learning (Aug 2021)
Mobyen Uddin Ahmed, Marcus Bengtsson, Antti Salonen, Peter Funk
International Congress and Workshop on Industrial AI (IAI2021)