Evolutionary computing and genetic algorithms: Paradigm applications in 3D printing process optimization

Abstract

3D printing is a relatively new group of manufacturing technologies, methods and processes that produce parts through material addition. 3D printing technologies are mainly employed for the fabrication of prototypes and physical models during product design and development; however as they continuously improve in terms of accuracy and range of raw materials they are increasingly employed in the actual manufacturing process. This puts a new emphasis on the study of some of the process planning problems and issues that are related with the cost efficient use of 3D printing systems and the quality of their products. Among the most crucial process planning problems are: (i) the selection of fabrication orientation and parameters which is by definition a multi-criteria optimization problem in which the operator seeks to achieve the optimum trade-off between cost and quality, under given fabrication constraints and requirements, and (ii) the batch selection/planning or “packing” problem, at which the selection and placement of various different parts inside the machine workspace is considered. As such, the primary goal of the chapter is to present the effective utilization of Genetic Algorithms, which are a particular class of Evolutionary Computing, as a means of optimizing the 3D printing process planning.