Additive Manufacturing: Equipment, Instrumentation and Measurement: Session II
Program Organizers: Ulf Ackelid, Freemelt AB; Ola Harrysson, North Carolina State University
Monday 2:00 PM
November 2, 2020
Room: Virtual Meeting Room 5
Location: MS&T Virtual
Session Chair: Timothy Horn, North Carolina State University
2:00 PM
Optical Emission Sensing for Laser-based Additive Manufacturing – What Are We Actually Measuring?: Christopher Stutzman1; Abdalla Nassar1; Wesley Mitchell1; 1Penn State University
Additive manufacturing is a rapidly growing industry in which complex components can be produced directly from a CAD file. While this process can easily produce complex components, the propensity for defect formation is not small. Therefore it is important to develop sensing procedures to ensure that quality components are produced. Traditionally, melt pool imaging has been a source of significant interest due to the assumption that one could measure the geometry of the melt pool and more fully understand the build process. Unfortunately, the co-axial camera data is not always interpreted correctly. Here, we show that under some conditions, melt pool measurements produced from a coaxial camera do not accurately estimate the melt pool. Further, using an off-axis camera filtered to observe excited titanium plume emissions, we show that the excited vapor plume above the melt pool significantly obscures measurements, particularly at high energy.
2:20 PM
Combining In-situ Monitoring and X-ray Computed Tomography to Assess the Quality of Parts Manufactured by Powder Bed Fusion: Philip Sperling1; Patrick Fuchs1; 1Volume Graphics GmbH
In recent powder bed fusion technologies for additive manufacturing different monitoring sensors are available. These monitoring technologies generate a huge amount of data during the production process. In the future this data could be used to avoid, reduce or precisely target following destructive or non destructive testing methods or even stop the production in an early stage. In the current state of insitu monitoring solutions the indications and process instabilities shown are very hard to interprete. Further investigations and correlations with post process testing measures are necessary. In our paper , we show how to correlate in situ monitoring datawith computed tomography results. For this, we produced two different sets of test pieces on metal powder bed fusion systems. Further, we show different approaches of analyzing the in situ data for process instabilities and how to use modern machine learning methods to correlate process signatures with part quality metrics.
2:40 PM
Characterization of 3D-printed Metals with Ultrasonic Technique: Terence Costigan1; Ping-Chuan Wang1; Rob Van Pelt2; 1SUNY New Paltz; 2Sono-Tek Corporation
Additive manufacturing (AM) of metals is rapidly finding applications in various industries due to its ability to make complex geometries and reduced prototyping cost. However, certain disadvantages inherited in AM prevent it from becoming widely adopted, including surface roughness, lack of isotropic densification and dimension accuracy, etc. Such imperfections have significant impact on product performance and reliability. The goal for this research is to utilize ultrasonic excitation as the means for diagnosing such impact. The methodology currently under development employs a pair of conical stainless steel specimens with piezoelectric transducers sandwiched in between to induce vibration. In this presentation, feasibility of proposed test and the specimen design considerations will first be demonstrated with finite element modeling. Experimental results comparing between specimens prepared by AM and traditional subtractive machining process will be summarized, and the implication on nondestructive ultrasonic characterization of AM structures will be discussed.
3:00 PM
In-Process Quality Control and Optimization for Ceramic 3D Printing: Zhaolong Zhang1; Richard Sisson1; Christopher Brown1; Jianyu Liang1; 1Worcester Polytechnic Institute
Advanced ceramics are widely used in aerospace, automotive, and other industries. Ceramic additive manufacturing Methods have been studied intensively in recent years due to the capability of producing complex products. However, the in-process control/feedback is not currently implemented in any commercially available ceramic 3D printer. In this project, in-process control/feedback is studied experimentally to improve control of the amount of material being deposited. Materials, processes, and machine parameters and their influence on the quality parameters are studied first. Robocasting is one of the most widely used additive manufacturing methods for a variety of ceramic-based materials at a low cost. This study uses robocasting as an example to implement an in-process control/feedback loop in a ceramic additive manufacturing process. Key parameters for ceramic robocasting are prescribed and a database of the relationship between these parameters and the quality of the printed part is established. Finally, a closed-loop robocasting process is described.