Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

Abstract

This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX.