# Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python
## 4 Results  
The specific quantitative results achieved through procedural generation in 3D modeling and printing demonstrate some reduction in the time spent on designing models. [PERSON] et al. [36] showed that procedurally generated 3D modeling algorithms diminished the time of designing models by up to 70%, especially considering complex architectural and game design models. Viewing costs, General Motors used procedural generation and achieved a 40% reduction in material costs and a 20% improvement in fuel efficiency [38]. After designing one single algorithm, it may generate many unique products. For example, when creating one ring, the algorithm can generate over 10,000 unique rings in minutes [39]. [PERSON] et al. [40] showed that using procedural generation to create customized prosthetics caused a 30% better match. Adidas presented that the usage of procedural generation caused a 60% reduction in the prototyping stage [41]. The University of Southern California showed that using procedural generation reduced the waste of materials by 50%. [PERSON] et al. [42] presented that using procedural generation algorithms, the components of aerospace machines were 25% stronger and 15% lighter [43]. Apple reported that procedural generation caused a 20% growth in production and a 10% reduction in defects [44]. Moreover, procedural generation caused a 50% reduction in house design costs and a 25% reduction in project realization time [44]. Some researchers analyzed statistical data on production efficiency and quality of products using procedural generation. [PERSON] et al. [36] analyzed the difference in time and cost of 50 projects (50% used traditional methods and 50% used procedural generation) and showed that the average design time of procedural generation was 10, whereas using traditional methods was 33 h (the difference in means--23 h). Moreover, using the t-test, procedural generation significantly reduces design time (\(p<0.001\)). General Motors Research & Development [38] examined 100 automotive parts that were redesigned using procedural generation and achieved a 40% reduction in cost. Moreover, they showed that the material cost reduction was statistically significant and defined a 95% confidence interval of 39.02% to 40.98%. Custom Jewelry Design Firms [39] studied 100 customers and showed that the average number of unique ring designs generated equals 10,000. After the analysis, they showed that procedural generation, considering enhanced customization, offers significantly higher customization options. Considering accuracy in fit and function, [PERSON] et al. [40] examined 30 patients with customized prosthetics and showed a 30% increase in fit accuracy. Thus, the fit accuracy improvement was statistically significant, with a 95% confidence interval of 28.57% to 31.43%. Viewing production efficiency, Adidas [41] studied 20 new shoe models and noticed a 60% reduction in the average prototyping time. Thus, the time reduction using procedural generation was statistically significant, with a 95% confidence interval of 57.38% to 62.62%. Waste reduction is vital, so the University of Southern California [42] studied 15 projects and showed a 50% material waste reduction. Thus, the waste reduction was statistically significant, with a 95% confidence interval of 47.47% to 52.53%. Considering quality and performance improvements, [PERSON] et al. [43] studied 40 structural components and achieved a 25% strength improvement and a 15% weight reduction. Using a \(t\)-test, both the strength improvement and weight reduction were statistically significant (\(p<0.001\)). Apple [44] studied consistency in production and examined 100 batches of internal components. They showed a 20% production consistency improvement and a 10% defect reduction. Hence, using a \(t\)-test, they observed that the improvements in production consistency and defect reduction were statistically significant (\(p<0.001\)).