An evolutionary neural network approach to machining process planning: A proof of concept

Abstract

Developments in high degree-of-freedom(DOF) manufacturing processes such as 5-axis machining and additive manufacturing have greatly moderated the design constraint and brought unprecedented manufacturing capability for parts in complex geometry. The advancement in manufacturing processes, at the same time, leads to significant challenges for process planning due to the increasing decision complexity. A method is needed to enable full automated process planning for high DOF manufacturing processes in the foreseeable future. This work focuses on exploring an artificial neural network(ANN) based approach for machining process planning, specifically the toolpath planning for milling operations. The objective of this research is to construct a framework for automated machining process planning that leverages the advancement in ANN methodologies in an attempt to generate an optimized toolpath without any human logic input. In this proposed framework, the voxel model is used as part design and stock geometry representations. An evolving ANN method NeuralEvolution of Augmenting Topologies(NEAT) is applied as the solution algorithm. A prototype implementation of the proposed framework is created and experimented with reasonably simplified machining scenarios and basic part geometries. Initial experiments demonstrate optimistic results supporting the feasibility of creating such an ANN through an evolutionary method to accomplish specific manufacturing requirements on different geometries. The work also revealed that the geometric input is a critical factor for successfully training an ANN model. Further work is needed to encode the part design geometric information as input. Additionally, an improved evolutionary ANN algorithm needs to be created to accelerate the model training.