Dynamic multi-objective optimisation for machining gradient materials

Efficient machining of material with continuously varying properties, so called gradient material, needs advanced planning of cutting parameters; this is a dynamic optimisation problem. Additionally the manufacturing has to satisfy several constraints in parallel. The parameters are optimised using a new predictive multi-objective optimisation approach based on Genetic Algorithms. The algorithm adapts online to the dynamically varying hardness properties of the material. A model based detailed case study is presented where the optimisation identifies good parameter sets for the machining. The solutions are finally selected based on a desirability function (DF) and heuristics.