ACIMO: Analyse acoustique intelligente de la machine

Situation

During the process of manufacturing components, tools undergo wear and tear depending on the level of usage and environmental conditions. Heavy wear to tools may result in components having inadequate surface properties and inaccurate dimensions. Heavy wear and its consequences arise if tools are not changed soon enough. Conversely, changing tools too early incurs higher tooling costs and means more process interruptions.

Course of action

The ACIMO concept aims to take measurements of the tools and to read the process parameters during the manufacturing process. Proven quality criteria of components, such as surface quality and dimensional accuracy, provide an indication of the wear and tear of tools.

The measurement data acquired will be used to create a model through machine learning which can determine a tool’s level of wear.

BFH is responsible for developing the measurement strategy and the machine learning model. A condition monitoring model will be created using the data produced, the physical knowledge available, the process data and the quality characteristics.

The project is led by the Haute Ecole Arc Ingénierie.