Conventional and AI-Assisted Topology-Driven Workflows for Injection-Molded Lightweight Structures: A Quantitative Case Study

The increasing availability of automated development workflows and data-driven methods raises the question of when approaches based on artificial intelligence (AI) provide potential benefits over established engineer-driven workflows in lightweight structural design. This paper presents a quantitative comparison between a conventional engineer-driven process and an AI-assisted, automated workflow for an injection-molded component with fixed installation space, identical boundary conditions, and manufacturing constraints. In the conventional process, topology optimization is followed by manual CAD reconstruction and iterative finite element analysis. In the AI-assisted process, an automated workflow generates many design variants that are simulated and used to train a regression-based surrogate model for rapid exploration of the design space. The conventional workflow yields a manufacturable structure with a high stiffness-to-mass ratio and controlled stresses, whereas the geometry selected from the surrogate model’s prediction shows reduced stiffness, higher stress peaks, and manufacturability issues. The analysis of the best-performing design identified ex post within the training data, rather than directly by the surrogate, illustrates the potential of the automated workflow but also highlights insufficient predictive accuracy for locally stress-sensitive quantities. On the process level, the AI-assisted workflow exhibits clear scaling advantages and a distinct break-even point in terms of development effort, suggesting that such methods are currently best suited as complementary tools for early-stage design space exploration. The quantitative effort values and the break-even point, however, are case-specific and should be interpreted as order-of-magnitude indicators rather than universally valid thresholds.