Mechanical Properties of Laser Beam Melting Components Depending on Various Process Errors
Additive Manufacturing processes are constantly gaining more influence. The layer-wise creation of solid components by joining formless materials allows tool-free generation of parts with very complex geometries. Laser Beam Melting is one possible Additive Manufacturing process which allows the production of metal components with very good mechanical properties suitable for industrial applications. These are for example located in the field of medical technologies or aerospace. Despite this potential a breakthrough of the technology has not occurred yet. One of the main reasons for this issue is the lack of process stability and quality management. Due to the principle of this process, mechanical properties of the components are strongly depending on the process parameters being used for production. As a consequence incorrect parameters or process errors will influence part properties. For that reason possible process errors were identified and documented using high resolution imaging. In a next step tensile test specimens with pre-defined process errors were produced. The influence of these defects on mechanical properties were examined by determining the tensile strength and the elongation at break. The results from mechanical testing are validated with microscopy studies on error samples and tensile specimens. Finally this paper will give a summary of the impact of process errors on mechanical part quality. As an outlook the suitability of high resolution imaging for error detection is discussed. Based on these results a future contribution to quality management is aspired.
Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images
Despite their advances and numerous benefits, metal powder-bed additive manufacturing (AM) processes still suffer from the high chances of defect formation and a need for improved quality. This work develops an online monitoring system for quality of fusion and defect formation in every layer of the laser powder-bed fusion process using computer vision and Bayesian inference. An imaging setup is developed that for the first time allows capturing in-situ (during the build) images from every layer that visualize detailed layer defects and porosity. A database of camera images from every layer of AM parts made with various part quality was created that is the first visual labeled dataset from in-situ visual images of the powder-bed AM (also visualizing detailed layer features). The dataset is used in training-based classification to detect layers or sub-regions of the layer with low quality of fusion or defects. Features are carefully selected based on physical intuition into the process and extracted from the images of the various types of builds. A Bayesian classifier is developed and trained to classify the quality of the build that signifies the defective and unacceptable build layers or regions. The results can be used for quasi-real-time (layer-wise) process control, further process decisions, or corrective actions.
Comparison of a Radiometric Procedure with Conventional Methods for Identification of Neisseria
A radiometric procedure was compared with the conventional cystine tryptic agar (CTA) sugar fermentation method for identification of Neisseria species. Four different ATCC cultures of Neisseria were identified by both procedures with identical results. The only difference noted was that the radiometric procedure required 3 h for completion, whereas the conventional CTA sugar method required overnight incubation. The radiometric procedure was also compared with the fluorescent antibody (FA) and CTA methods for identification of Neisseria gonorrhoeae. The organisms examined were gram-negative, oxidase-positive diplococci isolated from 49 clinical specimens sent to the laboratory for bacteriological analysis. Results obtained by both CTA and FA procedures were comparable. However, the radioisotope method appeared to be superior to the other two methods in that only one isolate identified as positive by both the CTA and FA methods was not identified radiometrically, whereas four isolates positive by the radiometric method were not identified by the other two procedures. Thus, a total of seven more positive identifications were made radiometrically than by either of the two other methods. All positive identifications were confirmed by a reference laboratory. These results indicate that the radiometric procedure is more rapid and reliable as compared with both the CTA and direct FA methods and, thus, may serve as a valuable addition to the methodology available for diagnostic microbiology.