  Optimizing manufacturing process parameters is typically a multi-objective
problem with often contradictory objectives such as production quality and
production time. If production requirements change, process parameters have to
be optimized again. Since optimization usually requires costly simulations
based on, for example, the Finite Element method, it is of great interest to
have means to reduce the number of evaluations needed for optimization. To this
end, we consider optimizing for different production requirements from the
viewpoint of a framework for system flexibility that allows us to study the
ability of an algorithm to transfer solutions from previous optimization tasks,
which also relates to dynamic evolutionary optimization. Based on the extended
Oxley model for orthogonal metal cutting, we introduce a multi-objective
optimization benchmark where different materials define related optimization
tasks, and use it to study the flexibility of NSGA-II, which we extend by two
variants: 1) varying goals, that optimizes solutions for two tasks
simultaneously to obtain in-between source solutions expected to be more
adaptable, and 2) active-inactive genotype, that accommodates different
possibilities that can be activated or deactivated. Results show that adaption
with standard NSGA-II greatly reduces the number of evaluations required for
optimization to a target goal, while the proposed variants further improve the
adaption costs, although further work is needed towards making the methods
advantageous for real applications.
