DESIGN COMPLEXITY AS A DRIVER FOR ADDITIVE MANUFACTURING PROCESS IMPROVEMENT

Abstract

Design complexity in additive manufacturing (AM) is a current issue in the research community, fueled by the well-known phrase “complexity for free”. This statement has promoted the assumption that complex geometries may be achieved without any increase in the cost of production. However, recent research has indicated that increasing shape complexity produces an increase in production costs for the material extrusion process. This challenges the mainstream assumption that AM technologies provide ‘complexity for free’. The AM community requires further investigation of design complexity and its impact on sustainable production when used as a Design for Manufacturing (DfM) tool. This paper proposes a data-driven method which uses design complexity as an AM performance indicator for the material extrusion process. The manufacturing responses included build time (BT), dimensional accuracy (DA) and complexity index (CI). Design space exploration of an automotive air filter model was achieved by varying five critical design features which impact complexity. The study utilized a Face Centered Central Composite Design (FCCCD) of three levels for the design features, comprising 32 experimental models. The optimal model was manufactured based on multi-objective optimization using the MINITAB© response optimizer. This method exploits the design features to achieve target performance and manufacturability. The viability of design complexity as an AM performance indicator was discussed leading to three major improvements to the Product Design and Development (PDD) process for AM. The proposed improvements have the potential to reduce process times and minimize resources, providing a sustainable AM approach for developing regions.