Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization

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.

Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning

Despite the great successes of machine learning, it can have its limits when dealing with insufficient training data.A potential solution is to incorporate additional knowledge into the training process which leads to the idea of informed machine learning. We present a research survey and structured overview of various approaches in this field. We aim to establish a taxonomy which can serve as a classification framework that considers the kind of additional knowledge, its representation,and its integration into the machine learning pipeline. The evaluation of numerous papers on the bases of the taxonomy uncovers key methods in this field.