AI for tear and wear management

In the world of machining, the chip removal tool is a critical point for efficiency at work, as well as for the balance of production costs. A poorly chosen tool can slow down times, increase energy consumption and produce greater wear and tear on the machine tool itself.

The development of Monitoring Systems for autonomous decision-making plays a fundamental role in the era of Industry 5.0, where the objective is to optimize production processes in all aspects automatically providing AI driven autonomous systems. In the case of the machining industry, the greatest losses are caused by downtime, and it is in this area where greater capacity for improvement can be achieved with the implementation of a human assist machines approach.

AIMAN aims at developing an AI driven system to improve the efficiency in the use of cutting tools. To achieve this overall aim, we have identified the following objectives:

1. To commence with a clear definition of the system specifications aligned with industrial requirements 
2. To design and carry out experimental test for the determination of important factor affecting the modelling and development of AI algorithms 
3. To design and implement a data ecosystem which will effectively treat the data collected and integrate it with the AI-driven system (Data collection, Pre-processing and cleaning of data, Select and build appropriate data traits: feature engineering, select a suitable model family, Optimise the hyperparameters of Machine Learning or Deep Learning models, Post-process, iterate and evaluate the AI models generated, Deploy learning models in third-party environments) 
4. To integrate and test the AI-driven system that will allow for:
   • Tool lifetime predictions
   • Recommendations of which tool is appropriate
   • Definition of the digital fingerprint of the cutting tool
   • Cutting speed and feed rate recommendations for the machining process
   • Always taking bearing in mind the specifications defined previously 
5. To integrate the system at WOLCO and carry out a comprehensive series of test in order to measure and document the technological and business indicators previously defined

Given that the goal is to ensure a business uptake of the results, the project will also ensure that via a robust approach to knowledge management, an effective IP protection strategy is in place and that will pave the way for commercial exploitation of the results (also via AI REGIO marketplace), as well as their dissemination to key stakeholders.

AIMAN will contribute towards complementing AI REGIO in terms of AI for manufacturing solutions as both, the problem to be solved and the proposed solution adds to the existing pilots. The complementary aspects include AI driven deep learning models. Moreover, AIMAN aligns with the European Commission definition on Industry 5.0 in that it will develop a system that not only will achieve growth for the economy but by optimising the use of the machining tools will minimise the use of planet resources and improve the quality of the manufactured products, hence, reducing scrap and faulty products. In terms of advancing the manufacturing sector in the use of AI, it is important for AIMAN that all AI-driven solutions comply with all 7 AI requirements for the fulfilment of the ethical and trustworthy guidelines set by AI HLEG.