Machine-configurable AI for industrial tool wearand process monitoring

Purpose and goal:

Tool wear is a key factor affecting the efficiency of industrial manufacturing processes and the surface quality of products. The project aim is to develop a completely new AI-driven service for tool wear and process monitoring based on IPercept´s patented self-diagnosis solution for automating diagnostics and condition monitoring in industrial machines.

The new service will contribute to the transformation of industries towards digitization and increase their competitiveness, easing the transition to smart maintenance and Industry 4.0. Expected results and effects:

The developed tool and process monitoring solution will enable optimization of cycle times, optimization of vibration during production to increase part quality, optimization of air cutting time and optimization of energy use during operation. This will also contribute to significant financial, environmental and operational gains for users.

The expected impacts for the industry are reduced machine damage, reduced production stoppages, reduced scrap parts and machining costs, and a reduced environmental footprint linked to reduced tool changes. Approach and implementation:

The solution will need to handle complexity and high variability in production, while providing automated insights to improve not only maintenance but also the operation of machines. To do so, the work plan has been developed into the following work packages: WP1 - Project management - definition of collaboration framework;

WP2 - Market research and dissemination; WP3 - Testing in a controlled environment - in collaboration with KTH; WP4 - Algorithm development - in collaboration with KTH; WP5 - Operational environment - pilot testing & training - in collaboration with Nord Lock.