A Framework for Predictive Decision Support System for Additive Manufacturing Techniques: Insights from Material Data

The industrial transformation of today depends heavily on additive
manufacturing because of its high resolution and accuracy. The most
widely utilized methods are stereolithography (SLA), selective laser
melting (SLM), selective laser sintering (SLS), and fused deposition
modeling (FDM). Since the advent of additive manufacturing, many
new materials have emerged in various formats, such as solid and
powder. With the increase in available materials, manufacturers
often struggle to choose the right techniques, as there are limited
methods and cost considerations to account for selecting the right
materials in additive manufacturing (AM) is crucial for improving
the performance, cost-efficiency, and the overall success of the final
product. The selection involves balancing costs with performance
requirements and is essential for achieving the desired performance,
ensuring process compatibility, controlling costs, and meeting
design and regulatory standards. The research paper focuses on
how to choose the appropriate technique for a given material using
a prediction decision support system, which has been developed
based on the provided material data to have better cost-efficiency.