Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data Integration are presented.
Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing (AM) technologies for the production of complex metallic real part components. Due to the multitude of factors influencing process conditions and part quality and due to the layer-wise characteristic of the process, monitoring of process signatures seems to be mandatory in case of the production of safety critical components. Here, the iterative process nature enables unique access for in-situ monitoring during part manufacture. In this talk, the successful test of the synchronous use of a high-frequency infrared camera and a camera for long time exposure, working in the visible spectrum (VIS) and equipped with a near infrared filter (NIR), will be introduced as a machine manufacturer independent thermal detection monitoring set-up. Thereby, the synchronous use of an infrared camera and a VIS NIR camera combines the advantages of high framerate and high spatial resolution. The manufacture of a 316L stainless steel specimen, containing purposely seeded defects and volumes with forced changes of energy inputs, was monitored during the build. The measured thermal responses are analysed and compared with a defect mapping obtained by micro X-ray computed tomography (CT).
The first results regarding methods for data analysis, derived correlations between measured signals and detected defects as well as sources of possible data misinterpretation are presented in this talk.
This presentation includes the results of a number of case studies of concrete properties using computed tomography (CT) in combination with various in-situ testing techniques, including those of mechanical loading, water transport, and fire. The results of these case studies demonstrate the potential of CT as an approach for obtaining unique, quantitative data about the structure of materials. This data can serve as the basis for calibrating and validating a new generation of numerical models that have a stronger foundation in micromechanical theory. This will contribute to the development of more accurate and versatile simulation approaches for concrete and other building materials.
Conventional concrete and UHPC performance-damage relationships identified using computed tomography
(2016)
To make significant advances in concrete engineering, it will be necessary to understand the behavior of cementitious materials at the microscale. To achieve this goal, the nature of damage initiation and growth needs to be understood at very small scales. This Research program sought to increase that understanding through the collection of microscale data using X-ray computed tomography (CT). The tensile and compression behavior of both ultra-high performance concrete (UHPC) and conventional concrete were investigated as a part of this
program. Relationships were identified between mechanical performance parameters, such as stiffness degradation and work of load, and cracking parameters, such as crack volume and crack surface area, that could be quantified mathematically and implemented into future finite element analysis (FEA) models. The results of this Research program have the potential to improve the accuracy and resiliency of numerical models and to provide insight to the materials engineering community concerning the optimal use of UHPC.