ITP Aero – ML-Based Turning Process Capability Improvement
ITP Aero
Sector: Industry
Business Case
The goal is to enhance the capability indices of the turning process (Cp, Cpk) through the application of ML techniques. Models based on data from the process itself will be used to make the process self-adaptive.
Objectives
Improve process capability (reduction of dimensional variability).
Use case
The process starts by identifying dimensions most likely to be addressed with this method. Process variables that may influence the variability of the selected dimensions are identified. The information is collected and stored. The variability is modelled and the model that reduces it is put into production. The process is repeated for additional variables.
Infrastructure
On Premise
Technology
Machine learning and deep learning
Data
Time series Private dataset
Resources
IT staff (2), quality (1), process engineers (1), manufacturing technologists (2) and data scientists (1).
Difficulties and learning
Dirty or non-existent required data. Low production volume. Complex process, combined work with process specialists is necessary. Time required to validate the improvement (given the volume of production).
KPIs (business impact and metrics of the model)
Cp, Cpk, Pp, Ppk
Funding
–
Collaborators, Partners
No
