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Artificial intelligence to boost competitiveness of industrial SMEs

Artificial intelligence to boost competitiveness of industrial SMEs
  • IDEKO participates in a European project aimed at developing integrated and cyber-physical systems to improve processes, maintenance and production of the plants of manufacturing SMEs

  • The research centre is involved in the initiative as a representative of the Basque Digital Innovation Hub, a network that seeks to provide Basque SMEs with the technological know-how to meet the challenges of Industry 4.0

  • The project focusses on the aviation sector, one of the most affected by the pandemic

The application of information and communication technologies in industrial manufacturing SMEs still has a lot of room for improvement. Little by little, manufacturing plants are implementing Industry 4.0 concepts and new developments into their production structure. However, they are still not capitalizing on the benefits of these solutions.

The research centre IDEKO, member of the Basque Research and Technology Alliance (BRTA), is participating in the European M-AID project, an initiative aimed at developing and implementing integrated and cyber-physical systems (CPES) driven by artificial intelligence with the aim of supporting manufacturing SMEs in terms of processes, maintenance and plant production.

The project has been running since the end of 2020 and will conclude in April 2022. The central idea is to apply systems using hybrid AI to provide information that is currently not accessible to the different participants in the production process such as planners, machine operators, quality control or maintenance staff.

“The CPES developed within the framework of the project will enable the cognitive optimisation of manufacturing plants by optimising process parameters, scheduling maintenance to maximise machine availability and organising plant production," says Alex Iglesias, project coordinator at IDEKO.

The technology centre specialising in Advanced Manufacturing participates in the project as a representative of the Basque Digital Innovation Hub (BDIH), a network that seeks to provide Basque SMEs with the technological know-how to meet the challenges of Industry 4.0.

BDIH's main activity in the initiative will be to provide the necessary facilities to execute the project, in addition to sharing M-AID developments with external SMEs, technology platforms and interested parties.

The technology developed during the project will be available at the BDIH for at least two years from the end of the project. This will make an extensive and long-lasting dissemination and exploitation of the project results possible.

“IDEKO, as a member of this network, aims to steer the digitisation of SMEs towards more knowledge-intensive manufacturing and higher value-added activities," adds Iglesias.

Specifically, the ICT and Automation research group of the company will contribute to the project with its experience in the development of technologies for advanced manufacturing, machine monitoring and Big-Data analysis. In addition, the Dynamics and Control department will share its knowledge in the dynamics of manufacturing processes to develop the artificial intelligence algorithms that will govern the CPES integrated in the production machines. Finally, the Manufacturing Processes group will focus on the optimisation of the production management of the plant and determine the control indicators.

The project consortium also includes Aeromec, a company specialising in the machining of components for the aeronautical sector, and Savvy, a company specialising in Big Data solutions for industrial manufacturing.

One step further in AI

For data processing, the M-AID initiative will integrate the latest ICT technology for capturing big data offered by machines and embedded systems, where hybrid AI will be used instead of traditional AI, combining predictive and prescriptive analytics.

To achieve this, data processing algorithms based on periodic tracking will be included, with the intention of obtaining the digital footprint of the machine and the process, as well as continuous monitoring to detect possible problematic scenarios during machining processes.

These monitoring procedures are supported by CPES providing different possibilities to extract detailed information. The implemented AI analytics system will analyse the large amount of incoming data from the production process, extract the most relevant information using methods such as dimensional reduction and apply learning algorithms to predict machine behaviour and optimise the process.

“This system will not only provide useful information about the quality of the process, but will also offer other variables not available until now, such as the wear rate of cutting tools in machining processes or a risk indicator for an upcoming machine stop or problem. Hence, the developed system will support decision making for process optimisation, predictive maintenance and production planning," adds XXX.

Focusing on the aeronautics sector

The use case of the M-AID project will focus on the aeronautics industry, as it has been one of the sectors most affected by the Covid pandemic. The current difficult situation presents a good opportunity for a relaunch based on the integration of digitised CPES. Specifically, the project will study the milling process of an aerospace compressor.

The aeronautics sector is a high added value industry, and of great importance for the European manufacturing industry. It provides work for 561,000 people and reached a turnover of €171.7 billion in 2018. The industry adheres to strict rules to ensure quality and reduce waste, often leading to inefficient manufacturing processes.

Although the use of CPES in aircraft is a common trend, the digital development of manufacturing processes lags behind and still employs traditional technology. Therefore, adapting the traditional supply chain by adopting the latest advances in digital manufacturing and prototyping has enormous potential to improve the production process without compromising quality and safety.

“Hence, the production process can be accelerated, resulting in increased efficiency and cost savings," Iglesias concludes.

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