Defect-based fatigue model for additive manufacturing

Additive Manufacturing Defect Detection using Neural Networks

Currently defect detection in additive manufacturing is predominately done by traditional image processing, approximation, and statistical methods. Two important aspects of defect detection are edge detection and porosity detection. Both problems appear to be good candidates to apply machine learning. This project describes the implementation of neural networks as an alternative method for defe...

Additive manufacturing of fatigue resistant materials: Challenges and opportunities

Reconstruction of an Extensive Midfacial Defect Using Additive Manufacturing Techniques.

Malignant peripheral nerve sheath tumors are extremely rare tumors arising in peripheral nerves. Only 17 cases involving the trigeminal nerve have ever been reported. These tumors have a very poor prognosis and very high rates of recurrence and metastases. Their recommended treatment involves complete tumor resection followed by radiation. This can be problematic in the head and neck region. We...

Implicit modeling for additive manufacturing

Additive Manufacturing (AM) technology distinguishes itself from more traditional fabrication processes by several significant factors. It allows the creation of objects with complex geometry that would not be possible by subtractive manufacturing or molding. Additionally, 3d printers are of much simpler use than other tools. Finally, since the cost per objects does not change depending on the ...

Topology Optimization for Additive Manufacturing

This paper gives an overview of the issues and opportunities for the application of topology optimization methods for additive manufacturing (AM). The main analysis issues discussed are: how to achieve the maximum geometric resolution to allow the fine features easily manufacturable by AM to be represented in the optimization model; the manufacturing constraints to be considered, and the workfl...