Time Estimation for Additive Manufacturing
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Additive Manufacturing (AM), also known as 3D printing or Rapid Prototyping (RP), refers to creation of solid objects with various complexities in a layer by layer fashion based on their 3D models. Time estimation for additive manufacturing is an essential requirement for production scheduling, machine selection, and cost estimation. Hence, the focus of this thesis is to build parametric model for time estimation in AM process. AM technology is increasingly becoming more efficient, available, and affordable. However, it is not yet as efficient as many traditional manufacturing processes such as casting and molding particularly when it comes to high volume production. Therefore, users should be provided with standard time and cost models such that they can conduct a comparative analysis when selecting manufacturing processes. The objective of this thesis is to identify the most influential geometric parameters that drive the overall print time and develop an empirical model for print time estimation. Also, the impact of geometric complexity on the print time is studied. For this purpose, multiple parts with different features and complexities are modeled in a CAD package. The parts are then made by the 3D printer and the print time is measured. Also, for collecting more data rapidly, print simulation is used. Multi-variable regression is used to determine the most influential parameters and the standard model for time estimation is generated accordingly. Eventually, the model is validated through comparing the generated estimates with the actual times measured on the 3D printer. The scope of this model is limited to particular part sizes and geometries. A secondary objective of this thesis is to conduct a predictive analysis about the future of the 3D printing technologies in different industries and applications. 3D printing technology has already demonstrated significant impacts in different industries sectors and will continue to be a game-changing technology in the years to come as the technology evolves.
CitationAmini, M. (2014). Time estimation for additive manufacturing (Unpublished thesis). Texas State University, San Marcos, Texas.