Geometric Complexity Evaluation Method for Adoption of Additive Manufacturing

The rapid evolution of additive manufacturing, commonly known as 3D printing, has spurred the need to determine when this technology should be favored over conventional or subtractive manufacturing techniques. Selecting additive manufacturing for a given part hinges on its complexity, as it must offer an economic advantage over traditional manufacturing methods. This paper introduces a method for assessing the geometric complexity of parts intended for additive manufacturing. While the prevalent approach in most industries relies on surface area to volume ratio as an indicator of complexity, our study highlights its limitations and potential for misleading manufacturers. In response, we propose a novel method that leverages machining surface generation and scrap ratio to evaluate part complexity. The effectiveness of this method is demonstrated through validation with real-world industrial parts and virtual manufacturing assessments, proving its superiority to the traditional surface area to volume ratio. One of the key advantages of our proposed method is its potential for automation, with the capability to analyze stereolithography (STL) files and estimate part complexity features. This automation has the potential to significantly reduce the cognitive load on additive manufacturing experts, making the technology more accessible and efficient. Overall, our research contributes to the critical decision-making process for selecting the most suitable manufacturing method, ensuring that additive manufacturing is chosen when it offers a distinct economic advantage driven by part complexity.