Pareto-Based Optimization of Part Orientation in Stereolithography

This paper presents a Pareto-based optimization algorithm to determine a series of best part orientations in stereolithography systems. Previous methods could apply limit objective functions (OFs). Moreover, methods with multiple OFs had abstracted them into a single fitness function. A single fitness function never reflects the characteristics of individual OFs properly. The method proposed here handles several OFs individually. The objective functions are the build time and support volume under a desired surface finish. The optimization was performed using the multi-objective genetic algorithm (MOGA). At each genetic algorithm step, the surface finish was achieved by applying the adaptive layer thickness method. Pareto-based optimization finds a series of best part orientations with minimum build time and volume support. It is a multi-objective optimization that handles complex CAD files. The algorithm was developed using MATLAB. The codes were run for some case studies and the results were very promising.