ABSTRACT
Over the years, rapid prototyping (RP) has been implemented in several companies in the manufacture of three-dimensional (3D) models. This process produces parts in a short time, in small quantities, and with complex geometry and is cheaper. RP can be divided into additive manufacturing and subtractive manufacturing, the former being the most used in 3D object printing. Additive manufacturing is a widely used process consisting of the adding of material layer by layer for building 3D objects from a model projected on a computer. This technology allows the printing of objects with complex shapes and is being increasingly adopted by the aircraft industry, jewelry, footwear, automotive, fashion products and medical implants, among others. When printing 3D objects, the build orientation optimization of 3D models influences the costs and surface quality. In this work, three build orientation optimization problems are studied: the single-objective problem, bi-objective problem and many-objective problem. To this end, three quality measures are applied: the support area, the build time and the surface roughness, for the car hoodvent model. First, a single-objective optimization problem is presented and solved by the genetic algorithm, obtaining optimal solutions for each objective function. Then, a study of the bi-objective optimization problem is carried out for each pair of objectives, and some representative trade-off solutions are identified. Finally, the study of the many-objective optimization problem, considering the three measures optimized simultaneously, is presented with some more optimal solutions found. The bi-objective and many-objective problems are solved by a multi-objective genetic algorithm. For a better analysis and comparison of the solutions found, Pareto fronts are used, enabling a better visualization of the solutions between the objectives. This chapter aims to assist the decision-maker in choosing the best part print orientation angles according to his/her preferences. The proposed approach found optimal solutions confirming its effectiveness.
