Optimizing 3D printing to reduce defects caused by uneven cooling

Team: Ismail Breiwish (ME), Yucao Ji (ME), Zhi-Wei Lin (ME), Lei Zhou (ME)
Advisor: Tarek Zohdi (ME)

The market size of the 3D printing industry has increased to 7.4 billion dollars in 2018, and users rely on 3D printers to achieve their ideas. However, most of the users seldom know what is the proper printing parameters that will give them the best printing qualities. Our team has worked on giving 3D printers the ability to learn from printing experience and to set the optimal parameters automatically so that users do not need to care about settings of their 3D printers. This new technology will make 3D printers more intelligent and more efficient.

Problem

Most of 3D printing users seldom know the proper printing parameters that will give them the best printing qualities. Common defects include deformation and lack of strength, both of which result from differences in temperature (i.e., temperature gradients) during the printing process.

Solution and Process

One way to reduce temperature gradients is by controlling the the temperature of the printer’s nozzle. This involves complex calculations pertaining to printing rate, printing path, material properties, and temperatures.

To predict the outcome and feedback through optimization algorithm, we constructed a finite volume model in STAR-CCM+. Within this model, parts are discretized by creating a mesh with nodes describing the geometry of the part. Partial differential equations (PDEs) dictate the physics of these systems and a system of PDEs can be solved to obtain information about the temperature field.

Genetic Algorithm is a search-based optimization technique based on the principles of Genetics and Natural Selection. Here, the genetic algorithm is applied for system parameter search without solving the complex physical model.

Results

• Quality Prediction
• Quality Improvement
• 3D Printing Automation