Recent advancements in stereolithography (SLA) and their optimization of process parameters for sustainable manufacturing

Abstract

Stereolithography employs digital models generated from computer-aided design to personalize 3D items automatically. Its development has made a substantial contribution to the aerospace, industrial, medical, manufacturing, design, and engineering sectors during the past 40 years. Excellent precision can be achieved by selecting the right process parameters and right material. That’s why this technology is highly valued. In this study, a comprehensive overview of Stereolithography, including its process, material overview, application, and recent advancements, is provided. The study includes a brief discussion of factors such as layer thickness, print orientation, curing time, curing temperature, and laser power, and how these parameters affect mechanical properties like material hardness, tensile strength at yield, flexural strength, elongation at break, and surface roughness. Furthermore, the paper discusses the application of optimization methods like Genetic Algorithms and Artificial Neural Networks (ANN) to analyze, refine, and determine the optimal processing parameters for stereolithography. These techniques play a vital role in achieving a diverse array of mechanical properties for manufactured products, thereby contributing to the advancement of additive manufacturing processes. Additive Manufacturing (AM) is essential in Industry 4.0 as it can produce customized products, lower costs for small and medium-sized production runs and has the ability to reuse materials. Most of the research has focused on current improvements, but there is a need for more attention to future work.

Keywords

• Stereolithography
• Material hardness
• Tensile strength
• Surface roughness
• ANOVA
• Genetic algorithm