In-Process X-ray Sensor for Additive Manufacturing

The demanding of innovative Non-Destructive Evaluation (NDE) in-process sensing technology on AM is cross-cutting and spans instrumentation, materials, processing, quality assurance, testing and modeling disciplines.nbsp; X-ray imaging and X-ray Diffraction (XRD) are traditional and standard NDE methods to detect defects and measure materials microstructure properties and other physical characteristics.nbsp; This SBIR Phase I proposal is to study the feasibility of using X-ray imaging and XRD based sensor to in-process monitor the quality of AM parts during the printing process and develop a solid quality assurance mechanism.X-ray imaging is an excellent tool to reveal internal features of materials, such as its application in dental clinical to detect tooth cracks. nbsp;XRD is a standard method to identify crystalline structures and to measure thenbsp;structurenbsp;and physical state of materials. nbsp;X-ray imaging can be used to directly detect pores and cracks for AM applications.nbsp; XRD can provide important microstructure information of the AM samples: such as crystal orientation, residual stress, chemical phase inhomogeneity, stacking faults and their arrangement, and concentrations of other defects. nbsp;It is well known that the performance of functional materials (components) is dependent on their microstructure.nbsp; The growth and formation of these materials are also closed related with their compound and microstructure. In principle, XRD can identify the root causes of the pores and cracks revealed by X-ray imaging.nbsp; Compared to non-contacting optical techniques, such as laser scanning with major goal for geometrical confirmation, this proposed in-process X-ray sensor has the potential to directly detect the defects and provide critical information related to the quality and performance of AM parts.nbsp; Furthermore, the real-time in-process sensor also brings the opportunity for effective process control and shed light on understanding the AM processes.