This study is a part of the ongoing project at the Division of Production and Materials Engineering titled “DynPCM: Dynamic Process Condition Monitoring for Brake Disk (BD) production line”.

According to the project description “cutting tools are one important part of the machining operation and cutting process. It is usually expensive because it consists of expensive and rare materials, making them "critical raw materials" and the technology to produce them is not cheap, in addition, it is fragile and prone to chemical interaction with the workpiece materials, which leads to rapid wear of the cutting edge and makes it the most vulnerable part of the process.”

This study addresses the development of tool condition monitoring (TCM) systems to monitor and control tool wear when machining GCI brake disks (BD) with a different set of sensors which is to be optimized.

·      Machining tests of BDs of different machinability with sharp and pre-worn tools (Experimental part)

·      Collection data on the cutting process by the array of sensors (including optical) (to be defined and justified/optimized during the study) (Experimental part)

·      Extract features from the given signals that are most important for process characterization and recognition of tool status. (Computational part)

The work is suitable for 1-2 students with a background in manufacturing systems, moderate knowledge of Matlab/Python. Some knowledges in Machine learning (or a huge motivation to learn ML/AI methods) are preferable. The study will be concluded as a written report of the Project Course or MSc thesis with an oral presentation of the results at LTH, LU.