Analytische Chemie
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The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
With rising popularity and availability of additive manufacturing (AM), companies mainly in the aerospace sector, set high requirements on quality control of AM parts, especially produced with selective laser melting (SLM). Since it was shown that those parts are prawn to flaws like pores or cracks, every part needs to be tested. Therefore, NDT Methods, like eddy current testing (ET), could help to characterize SLM parts. Research on ET has shown, that offline ET with high spatial resolution MR (magneto resistive) sensor arrays is possible and that flaws as small as 50 µm could be detected while significantly reducing testing time. A first approach on automated online ET method for testing SLM parts is proposed in this contribution.
Simple calibration concept of an online NMR module demonstrated in a modularised production plant
(2017)
Monitoring specific information (such as physico-chemical properties, chemical re-actions, etc.) is the key to chemical process control. Within the CONSENS Project, the challenge to adapt a commercially available benchtop NMR spectrometer to the full requirements of an automated chemical production environment was tack-led. The developed online NMR module was provided in an explosion proof housing and involves a compact 43.5 MHz NMR spectrometer together with an acquisition unit, a programmable logic controller for automated triggering, flow con-trol, as well as data communication.
First results of an aromatic coupling reaction in lab scale showed a general feasibil-ity according to the signal information in the acquired NMR spectra even though with a considerable overlap. Due to the comparatively low field strength of the NMR spectrometer multivariate methods had to be considered for the prediction of con-centration profiles based on spectral data. Typically, for industrial application of those methods, e.g. Partial Least Squares Regression (PLS-R) as well as Indirect Hard Modeling, large amount of calibration data is demanded, which must be ac-quired in time consuming lab-scale experiments and offline analytics. When it comes to changes in raw materials (e.g., varying functional groups, additional stabi-lizing agents) calibration experiments and data evaluation models are developed again. Here we present an approach of automated data analysis tools for low field NMR spectra with minimal calibration effort. The algorithms are based on Indirect Hard Modeling, whereby each component in each mixture spectra can be rep-resented by several flexible peak functions (pure component models). This means, that only pure component NMR spectra are needed to generate a first evaluation model. The flexibility of peak functions in the spectral model can be adjusted via constraints of peak parameters. The area of any pure component model can either be converted to concentrations based on a one-point calibration on raw material concentration or even neat solvent signals. In several cases it has been shown, the IHM works almost independently of the matrix of the real samples. Such a calibration can be repeated daily in the beginning of each process run with minimal time effort. Moreover, additional pure components can be added to the model or even substitut-ed while keeping the previously adjusted peak function constraints. The proposed method exhibited good agreement of resulting concentration data from low field NMR spectra, when compared to an online high field NMR spectrometer as refer-ence instrument.
Currently research in chemical manufacturing moves towards flexible plug-and-play approaches focusing on modular plants, capable of producing small scales on-demand with short down-times between individual cam-paigns. This approach allows for efficient use of hardware, a faster optimization of the process conditions, and thus, an accelerated introduction of new products to the market. Driven mostly by the search for chemical syntheses under biocompatible conditions, so-called “click” chemistry rapidly became a growing field of research. The re-sulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via compara-bly straightforward and robust analysis techniques. Here we report on a fast and reliable calibration-free online high field NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement with a time interval of 20 s per spectrum, and a robust, automated algo-rithm to interpret the ob-tained data. All spectra were acquired using a 500 MHz NMR spectrometer (Varian) with a dual band flow probe having a 1/16 inch polymer tubing working as a flow cell. Single scan 1H spectra were recorded with an acquisition time of 5 s, relaxation delay of 15 s.
Currently research in chemical manufacturing moves towards flexible plug-and-play approaches focusing on modular plants, capable of producing small scales on-demand with short down-times between individual cam-paigns. This approach allows for efficient use of hardware, a faster optimization of the process conditions, and thus, an accelerated introduction of new products to the market. Driven mostly by the search for chemical syntheses under biocompatible conditions, so-called “click” chemistry rapidly became a growing field of research. The re-sulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via compara-bly straightforward and robust analysis techniques. Here we report on a fast and reliable calibration-free online high field NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement with a time interval of 20 s per spectrum, and a robust, automated algo-rithm to interpret the ob-tained data. All spectra were acquired using a 500 MHz NMR spectrometer (Varian) with a dual band flow probe having a 1/16 inch polymer tubing working as a flow cell. Single scan 1H spectra were recorded with an acquisition time of 5 s, relaxation delay of 15 s.
Hydroformylation in microemulsions under mild reaction conditions is investigated using a Rhodium-based catalyst yields high selectivity of 98:2 of the desired linear product. A successful mini-plant operation for over 130 hours was performed. Online concentration monitoring of the system was done using 785nm Raman spectrometer.
The design of sample flow cells, commonly used in online analytics and especially for medium resolution NMR spectroscopy (MR-NMR) in low magnetic fields, was experimentally and theoretically investigated by 1H-NMR and numerical simulations. The flow pattern was characterised to gain information about the residence time distribution and mixing effects. Both 1H-NMR imaging and spectroscopy were used to determine the characteristics of flow cells and their significance for on-line measurements such as reaction monitoring or hyphenated separation spectroscopy. The volume flow rates investigated were in the range from 0.1 to 10 ml/min, typically applied in the above mentioned applications. When compared to those commonly used in high-field NMR, the special characteristics of flow cells for MR-NMR were revealed by various NMR experiments and compared with CFD simulations. The influence of the design of the inlet and outlet on the flow pattern was investigated as well as the effect of the length of the cell. For practical use, a numerical estimation of the inflow length was given. In addition, it was shown how experiments on the polarisation build-up revealed insight into the flow characteristics in MR-NMR.
Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced.