Modelling and optimizing processes applicable in maintenance
Project label
SV001
Project duration (years)
2019.-2021.
Project abstract
Scientific research within the project is based on the application of designed experiments and statistical methods as well as methods of artificial intelligence with the aim of modelling and optimizing the processes applicable in maintenance (thermal spraying, machining).
The subject of research also refers to the processing of images of machined surfaces by artificial intelligence methods, in terms of predicting the output variable (surface roughness) with or without optimization of machining parameters and in terms of classification according to the output variable (surface roughness) with or without optimization of machining parameters.
Project aim
The main aim of the project is to derive statistical quantitative models for predicting mass loss due to wear and surface roughness and determine the optimal values of thermal spraying and machining parameters. The proposed models and optimal parameter values can be used after testing as a support for maintaining various components or finished products in order to reduce maintenance time and costs. The aim is also to use artificial intelligence methods to obtain models for predicting and/or classifying the obtained surface roughness based on the input machining parameters that could be subsequently optimized with regard to the current priority objectives of the machining process.