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Global demand for pigments is expected to increase to around 13.4 million tons per year by 2030, representing approximately $22 billion – 5% of which will be azo pigments as the main organic pigments. Azo pigments, i.e., especially the azo bridge required for them, are almost exclusively produced in technology by the so-called azo coupling reaction in large-scale approaches on the scale of 50–100 cubic meters. Among other things, one of the reasons why large-scale production itself is still essentially discontinuous is the lack of analytical methods for a continuous azo pigment synthesis.
In an azo coupling process, an aromatic amine is diazotized to form the diazonium compound (diazo compound), which then reacts with a coupling component, i.e., "couples". A slight excess of the coupler is desired for the end of the reaction, which stoichiometrically consumes the remaining (harmful) diazo compound, i.e., below 500 ppm. This is carried out with a manual spotting reaction of a fast-binding coupler.
To carry out continuous azo coupling, care must be taken to ensure that the actual coupling reaction proceeds as rapidly as possible, i.e., preferentially, compared with possible side reactions. Reliable measuring methods are thus a decisive criterion for the useful performance of a continuous azo pigment synthesis. An analytical online procedure is difficult due to several limitations at once: solid dispersions extremely limit the range of reproducible analytical procedures. In addition, the appearance of a signal (e.g., excess of a starting material) in the ppm range must be reliably detected against the matrix background.
The back-titration method and benchtop NMR spectroscopy were used to overcome these problems. We present a feasibility study for a suitable process analysis with benchtop NMR spectroscopy on disperse systems for equimolar dosing of the "diazotization" or "coupling" sub-step in azo coupling, which was carried out in cooperation with a chemical company. This could replace tedious manual spotting
At the Workshop "Neutron and Synchrotron Monitoring in Aerospace Advanced Manufacturing" at the Institute of Materials Physics in Space, German Aerospace Center (DLR) in Cologne, we presented on the opportunities and our experiences of using a powder based additive manufacturing process for in-space manufacturing applications in microgravity.
The rising popularity of additive manufacturing processes leads to an increased interest in possibilities and methods for related process monitoring. Such methods ensure improved process quality and increase the understanding of the manufacturing process, which in turn is the basis for stable component quality, e.g., required in the aerospace industry or in the medical sector. For laser powder bed fusion, a handful of process monitoring tools already exist, such as optical tomography, thermography, pyrometry, imaging, or laser power monitoring. Although these tools provide helpful information about the process, more information is required for an accurate in-depth understanding. In this article, advanced approaches in eddy current testing (ET) are combined, such as single wire excitation, magnetoresistive (MR) sensor arrays, and heterodyning to build up a system that can be used for online process monitoring of laser powder bed fusion. In addition to detailed information about the developed ET system and underlying signal processing, the first results of magnetoresistance-basedonline ET during the laser powder fusion process are presented. While producing a step-shaped cuboid, each layer is tested during recoating.
Test results show that not only the contours of the topmost layer are detected but also the contours of previous layers covered by powder. At an excitation frequency of 1 MHz, a penetration depth of approx. 400 μm is obtained. To highlight the possibilities of ET for online process monitoring of laser powder bed fusion, results are compared with postexposure images of the integrated layer control system (LCS).
Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete
(2022)
Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head.
Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete
(2022)
Freeform additive manufacturing of concrete structures is a rising technology in civil engineering with several fascinating advantages. Nonetheless, to ensure reliability and structural integrity, standards and quality control are required in the future to bring this technology into the market. As the concrete is manufactured continuously, continuous quality control of the printing process is also required, i.e. comprehensive process monitoring. At BAM, a test rig will be installed, enabling the printing of concrete structures with a maximum size of 2 m x 1 m x 1 m (l x w x h). Here, process monitoring is the focus of the test rig. In this study, we show the results of the first pump tests, including the measurement of several parameters such as temperature and pressure along the supply system, i.e. from the concrete pump to the printer head.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the
quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
Results of the project ProMoAM (Process monitoring in additive manufacturing) presented. Results from in-situ eddy current testing, optical emission spectroscopy, thermography, optical tomography as well as particle and gas emission spectroscopy are summarized and correlated to results from computed tomography for future in-situ defect detection.
Additive manufacturing of metals gains increasing relevance in the industrial field for part production. However, especially for safety relevant applications, a suitable quality assurance is needed. A time and cost efficient route to achieve this goal is in-situ monitoring of the build process. Here, the BAM project ProMoAM (Process monitoring in additive manufacturing) is briefly introduced and recent advances of BAM in the field of in-situ monitoring of the L-PBF and the LMD process using thermography are presented.