In-line Process Monitoring Research Articles

In-Line Monitoring of Downstream Purification Processes for VSV Based SARS-CoV-2 Vaccine Using a Novel Technique.

The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) increases the need for a rapid development of efficient vaccines. Among other vaccines in clinical trials, a recombinant VSV-∆G-spike vaccine was developed by the Israel Institute for Biological Research (IIBR) and is being evaluated. The development of an efficient downstream purification process (DSP) enables the vaccine to be advanced to clinical trials. The DSP must eliminate impurities, either process- or product-related, to yield a sufficient product with high purity, potency and quality. To acquire critical information on process restrictions and qualities, the application of in-line monitoring is vital and should significantly impact the process yield, product quality and economy of the entire process. Here, we describe an in-line monitoring technique that was applied in the DSP of the VSV-∆G-spike vaccine. The technique is based on determining the concentrations of metabolites, nutrients and a host cell protein using the automatic chemistry analyzer, Cobas Integra 400 Plus. The analysis revealed critical information on process parameters and significantly impacted purification processes. The technique is rapid, easy and efficient. Adopting this technique during the purification process improves the process yield and the product quality and enhances the economy of the entire downstream process for biotechnology and bio pharmaceutical products.

Optical sensor arrays designed for guided manufacture of perfluorocarbon nanoemulsions with a non-synthetic stabilizer

Hydrophobic drugs are incorporated into oil-in-water nanoemulsions (OIW) either as new formulations or repurposed for intravenous delivery. Typically, these are manufactured through stepwise processes of sonication or high-pressure homogenization (HPH). The guiding criteria for most nanoemulsion manufacture are the size and homogeneity/polydispersity of the drug-laden particles with strict requirements for clinical injectables. To date, most formulation optimization is done through trial and error with stepwise sampling during processing utilizing dynamic light scattering (DLS), light obscuration sensing (LOS) or laser particle tracking (LPT) to assess manufacturing progress. The objective of this work was to develop and implement an in-line optical turbidity/nephelometry sensor array for the longitudinal in-process monitoring of nanoemulsion manufacture. A further objective was the use of this sensor array to rapidly optimize the manufacture of a sub-120 nm oxygen carrying perfluorocarbon nanoemulsion with a non-synthetic stabilizer. During processing, samples were taken for particle size measurement and further characterization. There was a significant correlation and agreement between particle size and sensor signal as well as improved process reproducibility through sensor-guided manufacture. Given the cost associated with nanoemulsion development and scale-up manufacture, our sensor arrays could be an invaluable tool for efficient and cost-effective drug development. Sensor-guided manufacturing was used to optimize oxygen-carrying nanoemulsions. These were tested, in vitro, for their ability to improve the viability of encapsulated endocrine clusters (mouse insulinoma, Min6) and to eliminate hypoxia due to oxygen mass transfer limitations. The nanomulsions significantly improved encapsulated cluster viability and reduced hypoxia within the microcapsule environment. Statement of significanceNanoemulsions are rapidly becoming vehicles for the controlled release delivery of both hydrophilic and hydrophobic drugs given their large surface area for exchange. As work shifts from bench to large scale manufacturing, there is a critical need for technologies that can monitor and accumulate data during processing, particularly regarding the endpoint criteria of particle size and stability. To date, no such technology has been implemented in nanoemulsion manufacture. In this paper we develop and implement an optical sensor array for in-line nanoemulsion process monitoring and then use the array to optimize the development and manufacture of novel reproducible oxygen carrying nanoemulsions lacking synthetic surfactants.

OCT Capillary Depth Measurement in Copper Micro Welding Using Green Lasers

The transition of the powertrain from combustion to electric systems increases the demand for reliable copper connections. For such applications, laser welding has become a key technology. Due to the complexity of laser welding, especially at micro welding with small weld seam dimensions and short process times, reliable in-line process monitoring has proven to be difficult. By using a green laser with a wavelength of λ=515 nm, the welding process of copper benefits from an increased absorption, resulting in a shallow and stable deep penetration welding process. This opens up new possibilities for the process monitoring. In this contribution, the monitoring of the capillary depth in micro copper welding, with welding depth of up to 1 mm, was performed coaxially using an optical coherence tomography (OCT) system. By comparing the measured capillary depth and the actual welding depth, a good correlation between two measured values could be shown independently of the investigated process parameters and stability. Measuring the capillary depth allows a direct determination of the present aspect ratio in the welding process. For deep penetration welding, aspect ratios as low as 0.35 could be shown. By using an additional scanning system to superimpose the welding motion with a spacial oscillating of the OCT beam perpendicular to the welding motion, multiple information about the process could be determined. Using this method, several process information can be measured simultaneously and is shown for the weld seam width exemplarily.

Characterization of process and machine dynamics on the precision replication of microlens arrays using microinjection moulding

Injection moulding has shown its advantages and prevalence in the production of plastic optical components, the performance and functionality of which rely on the precision replication of surface forms and on minimizing residual stress. The present work constitutes a systematic and comprehensive analysis of practical microlens arrays that are designed for light-field applications. Process parameters are screened and optimized using a two-stage design of experiments approach. Based on in-line process monitoring and a quantitative and qualitative evaluation being carried out in terms of geometric accuracy, surface quality and stress birefringence, the replication is shown to relate directly to machine settings and dynamic machine responses. The geometric accuracy and stress birefringence are both largely associated with screw displacement and peak cavity pressure during the packing stage, while surface quality is closely related to cavity temperature. This study provides important insights and recommendations regarding the overall replication quality of microlens arrays, while advanced injection moulding solutions may be necessary to further improve the general replication quality.

Testing the Limits of a Portable NIR Spectrometer: Content Uniformity of Complex Powder Mixtures Followed by Calibration Transfer for In-Line Blend Monitoring.

(1) Background: Portable NIR spectrometers gain more and more ground in the field of Process Analytical Technology due to the easy on-site flexibility and interfacing versatility. These advantages that originate from the instrument miniaturization, also come with a downside with respect to performance compared to benchtop devices. The objective of this work was to evaluate the performance of MicroNIR in a pharmaceutical powder blend application, having three active ingredients and 5 excipients. (2) Methods: Spectral data was recorded in reflectance mode using static and dynamic acquisition, on calibration set samples developed using an experimental design. (3) Results: The developed method accurately predicted the content uniformity of these complex mixtures, moreover it was validated in the entire calibration range using ±10% acceptance limits. With respect to at-line prediction, the method presented lower performance compared to a previously studied benchtop spectrometer. Regarding the in-line monitoring of the blending process, it was shown that the spectral variability-induced by dynamic acquisition could be efficiently managed using spectral pre-processing. (4) Conclusions: The in-line process monitoring resulted in accurate concentration profiles, highlighting differences in the mixing behaviour of the investigated ingredients. For the low dose component homogeneity was not reached due to an inefficient dispersive mixing.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models.

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding. The RTD was determined using the active pharmaceutical ingredient (API) as a tracer with multiple designs of experiment (DoE) studies to determine the effect of critical process parameters (CPPs) on the process dynamics. To achieve quality control through material diversion from feeding data, soft sensor-based process control tools were designed using the RTD model. The operation block model of the system was designed to select feasible experimental setups using the RTD model, and feeder characterizations as digital twins, therefore visualizing the output of theoretical setups. The concept significantly reduces the material and instrumental costs of process design and implementation.

Development of chemometric models using Vis-NIR and Raman spectral data fusion for assessment of infant formula storage temperature and time

This study evaluated the potential of Vis-NIR and Raman spectral data fusion combined with PLS and SVM chemometric models developed using a large dataset (n = 1700) of commercial infant formula (IF) samples to (i) discriminate between different IF storage temperature (20, 37 °C) and (ii) predict IF storage time (0–12 months). Three interval-based PLS variable selection methods (forward interval PLS (FiPLS), backward interval PLS (BiPLS) and synergy interval PLS (SiPLS)) and SVM-recursive feature elimination (SVM-RFE) methods were compared for model development. The best IF storage temperature discrimination model was developed using SVM classification (SVMC) and Vis-NIR spectra (400–2498 nm) (AccuracyCV = 99.82%, AccuracyP = 100%). SVM regression (SVMR) models developed using medium level data fusion (features selected by SVM-RFE) had the lowest root mean square error (RMSE) values for IF samples stored at either temperature, 20 °C or 37 °C (RMSECV = 0.7–0.8, RMSEP = 0.6–0.9). Industrial relevanceSpectroscopic technologies, including Vis-NIR and Raman spectroscopy have been widely applied for process analysis and increasingly for on-line process monitoring in areas of chemicals, food processing, agriculture and pharmaceuticals, etc. Due to their rapid measurement and minimal or no sample preparation, they are highly suitable for in-line process monitoring. This study demonstrates that Vis-NIR and Raman process analytical tools either individually or combined may be employed for quality assessment and process control of IF manufacture.

Precision replication of microlens arrays using variotherm-assisted microinjection moulding

Injection moulding is ubiquitous in the production of plastic optical components, the functionality of which depends on minimising residual stress and on precisely replicating surface forms. In this study, variotherm-assisted microinjection moulding is used to manufacture a practical microlens array designed for light-field applications in order to achieve satisfactory geometric accuracy, surface quality and stress birefringence of the microlenses, all of which are difficult to achieve by conventional microinjection moulding. Based on in-line process monitoring and a comprehensive evaluation regarding the quantitative and qualitative aspects of the aforementioned factors, the replication is shown to correlate well with machine settings and dynamic machine responses. Screw movements are found to be crucial in the replication process. Compared to an optimised conventional microinjection moulding condition, the general residual stress level and uniformity of the microlens array area are improved by 5.08% and 88.11%, respectively, when the warm circuit temperature is 135 °C and enhanced by 2.73% and 84.32%, respectively, by a delayed switch from the warm circuit to the cold circuit. It is concluded that variotherm-assisted microinjection moulding can significantly reduce residual stresses while maintaining excellent geometric accuracy and surface quality.

Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys

Friction stir welding process has been studied extensively in the last decades since its early stage. Most of the research done so far is related to the process development including tool design, material weldability, post-weld mechanical behavior, and microstructural properties. More recently, in-line process monitoring and artificial intelligence algorithms are introduced into this process, but mainly to specific material configuration and joint thicknesses. This study will focus on the evaluation of different machine learning approaches including principle component analysis, K-nearest neighbor, multilayer perceptron, single vector machine, and random forest methods on a friction stir welding cell environment. The input variables provided from this cell environment are namely divided into two groups: one group refers to the application variables and the other group is related to the friction stir welding process variables. The application variables target the aluminum alloys, joint configuration, sheet thicknesses, initial mechanical properties, and their chemical composition. The friction stir welding process variables dictate the rotational speed, travel speed, forging force, longitudinal and transverse forces, torque, and specific energy. The output response to model from these machine learning algorithms is the defect index, which has been quantified using high-resolution immersed bath ultrasounds. This nondestructive evaluation technique has been described previously, which can detect defects ≥150 µm in thin sheets. The defect index has been classified into five classes, which is distinguished by the nature of defect, cold weld, or hot weld, as well as the width of the internal volumetric defect upon ultrasound C-scan result. The dataset, which is composed of around 500 various process conditions, has been generated over the last few years and the variables were taken exclusively in constant weld regime and in the force control mode using the output average values. This paper compares the best resulting machine learning methods applied on a friction stir welding cell basis, which is the K-nearest neighbor and multilayer perceptron algorithms. The K-nearest neighbor model reaches a deviation of 0.55 on the defect index in comparison with the experimental values, which is slightly better than the multilayer perceptron model, which obtains a score of 0.69. Over the initial 59 available model parameters, 10 and 15 of them were retained in the final algorithm using these techniques. The main predictors include the material thickness, base material ultimate tensile stress, rotational speed, travel speed, weld forces, and specific energy. The K-nearest neighbor model was able to provide a map of defect indices with regard to rotational speed and travel speed but was only possible when a higher density of data was found within the prediction area. A data density score was also included within the model to inform the end-user about the prediction reliability. The machine learning models are mainly about differentiating various cases rather than representing the physical phenomena as determined using the finite element analysis. That being said, in order to improve the prediction reliability as well as the machine learning models, the data twinning concept, which consists of generating simulated friction stir welding process conditions by finite element analysis, is briefly discussed.

Concentrated Nonequilibrium HD for the Cross Calibration of Hydrogen Isotopologue Analytics

For the fuel cycles of fusion power plants, highly specialized in-line analytic systems are crucial for efficient process control, monitoring, and accountancy. One of these systems under development is infrared (IR) absorption spectroscopy of liquid hydrogen isotopologue mixtures that can be used for in-line process control and monitoring of cryogenic distillation. The main challenge of this method is the complex calibration procedure since the integral IR absorption strength is nonlinearly correlated with the isotopologue composition. Typical calibration procedures make use of well-known samples produced by mixing atomic pure samples and referenced by p-V-T-measurement. The samples are catalyzed to produce samples containing heteronuclear molecules. By this procedure, one cannot exceed the chemical equilibrium of high temperatures (mass action coefficient Kc<4). Therefore, it is not possible to produce samples with an HD, HT, or DT concentration above 50% by catalysis or natural equilibration. However, in isotope or isotopologue separation, such as in cryogenic distillation, this equilibrium will be regularly exceeded. In the case of IR absorption spectroscopy on liquid hydrogen isotopologues, additional care needs to be taken for calibration since the calibration functions are highly nonlinear. We tested our calibration in the high-purity HD regime (Kc>4) by producing a sample via cryogenic distillation and performing a cross calibration for three systems: Quadrupole mass spectrometry, Raman spectroscopy, and infrared spectroscopy. Therefore, we can also demonstrate that additional calibration points are indispensable in order to improve the systematic uncertainties below the 5% level, and a simple extrapolation from a calibration of Kc < 4 to Kc > 4 will result in a trueness and accuracy exceeding this 5% level.

An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.

Manufacture and characterization of two distinct quasi-polymorphs of empagliflozin

Abstract We investigated the polymorphic behaviors of empagliflozin (EMP), an SGLT2 inhibitor focusing on the preparation and characterization of its two distinct quasi-polymorphs combined with in-line monitoring of crystallization processes. EMP has only been reported as a single polymorph without pseudo-polymorphs so far. In this study, two differentiable crystal forms, the needle-shaped ‘Form I’ and platy-shaped ‘Form P’, were reproducibly created using an anti-solvent approach. Although the two forms of EMP yielded the same results in many of these analyses, they could be differentiated by powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and microscopy. Based on a detailed analysis of DSC thermograms, a small but obvious transition was detected during the melting of crystalline EMP at 152–157 °C. This transition, which occurred at a low activation energy, was identified as a polymorphic transformation from Form P to Form I. The anti-solvent crystallization process was monitored by in-line NIR absorbance measurements and interpreted using principal component analysis (PCA). The crystal form was dependent on the crystallization solvent. Two different solvent/anti-solvent pairs (ethanol-water and ethanol-hexane) yielded substantially different PCA results, indicating distinct pathways to the creation of each crystal form. Rapid nucleation and high crystal growth rate was achieved by crystallization under Scheme P, which also exhibited fluctuation in its PC score plot. This was associated with reversible polymorphic selection between the two crystal forms during the crystallization of EMP.

An automatic computer vision pipeline for the in-line monitoring of freeze-drying processes

Infrared imaging sensors were proposed in the past years for the real-time monitoring and control of the Vacuum Freeze Drying (VFD) process. In order to extract reliable, real-time, and quantitative features from images collected by these sensors, a robust and automated image processing pipeline is required. Two major issues that have to be addressed concern the object detection and segmentation step, which locates and segments the objects whose temperature needs to be measured, and the tracking step, which is about following the movement of objects of interest to correlate information across subsequent images. Traditional intensity-based image analysis techniques are not reliable in this specific application, since the temperature, which is the intensity of a thermal picture, varies with time, whereas deep learning-based techniques are more robust to such changes.In this work, an object detector network based on a Faster Region Convolutional Neural Network (Faster R-CNN), and a Kernelized Correlation Filter (KCF) tracker were combined to monitor the VFD process of products contained in glass vials, which is a common scenario in the pharmaceutical domain. The object detector was trained on nine experimental acquisitions (7981 images) and tested on nine more (7201 images). Data augmentation methods consisting in the generation of synthetic sequences were used to effectively increase the performance while reducing the cost of data acquisition and annotation. The proposed technique achieved a recall and precision equal to 99.6 %, with execution time compatible with a real-time application. The localization of the vials was precise enough to measure the mean temperature with an acceptable error.

Quantitative Raman spectroscopy for the Ioncell\u2122 process. Part 1: comparison of univariate and multivariate calibration methods for the quantification of water and protic ionic liquid components

We investigate in this paper the potential of Raman spectroscopy for the quantification of protic ionic liquid components (acid and base) and water, in ionic liquid/water mixtures, taking 1.5-Diazabicyclo[4.3.0]non-5-enium acetate ([DBNH][OAc]) as a case study. We show that the combination of Raman spectroscopy and chemometrics is quite successful for the quantitative analysis of the ionic liquid components and water in mixtures over wide concentration ranges. The finding of the present work suggest that Raman spectroscopy should be considered more universally for the in-line monitoring and control of processes involving ionic liquid/H2O mixtures.

Towards a novel continuous HME-Tableting line: Process development and control concept

The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.

Sub-second quantum cascade laser based infrared spectroscopic ellipsometry.

Laser-based infrared spectroscopic ellipsometry (SE) is demonstrated for the first time, to the best of our knowledge, by applying a tunable quantum cascade laser (QCL) as a mid-infrared light source. The fast tunability of the employed QCL, combined with phase-modulated polarization, enabled the acquisition of broadband (900-1204 cm-1), high-resolution (1 cm-1) ellipsometry spectra in less than 1 second. A comparison to a conventional Fourier-transform spectrometer-based IR ellipsometer resulted in an improved signal-to-noise ratio (SNR) by a factor of at least 290. The ellipsometry setup was finally applied for the real-time monitoring of molecular reorientation during the stretching process of an anisotropic polypropylene film, thereby illustrating the advantage of sub-second time resolution. The developed method exceeds existing instrumentation by its fast acquisition and high SNR, which could open up a set of new applications of SE such as ellipsometric inline process monitoring and quality control.

In situ and remote laser diagnostics for material characterization from plasma facing components to Cultural Heritage surfaces

Optical and spectroscopic techniques offer unique possibilities for non destructive or micro-destructive characterization of surfaces, with widespread applications, starting from in-line monitoring of industrial processes. A significant group of industrial applications concerns nuclear grade material characterization and plasma diagnostics relevant to the thermonuclear fusion process. However, technologies and methodologies originally developed for specific in-vessel utilization, can easily find additional in-situ and remote application to environmental and cultural heritage (CH) diagnostics addressed to preventive conservation and restoration of surfaces. At ENEA Frascati different prototypes have been developed and patented to collect reflectance and fluorescence images excited at different ultraviolet and visible laser wavelengths, Raman and LIBS signals. Portable integrated instruments suitable for operation at different distances from a 1.5 to 30 m, have been assembled and operated in laboratory on multilayered samples and in field campaigns for Security and on CH painted surfaces. Significant results relevant to the cross fertilization among different applications will be presented and discussed.

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame.

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization-Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.

Springtail-Inspired Triangular Laser-Induced Surface Textures on Metals Using MHz Ultrashort Pulses

Considering the attractive surface functionalities of springtails (Collembola), an attempt at mimicking their cuticular topography on metals is proposed. An efficient single-step manufacturing process has been considered, involving laser-induced periodic surface structures (LIPSS) generated by near-infrared femtosecond laser pulses. By investigating the influence of number of pulses and pulse fluence, extraordinarily uniform triangular structures were fabricated on stainless steel and titanium alloy surfaces, resembling the primary comb-like surface structure of springtails. The laser-textured metallic surfaces exhibited hydrophobic properties and light scattering effects that were considered in this research as a potential in-line process monitoring solution. The possibilities to increase the processing throughput by employing high repetition rates in the MHz-range are also investigated.

Inline weld depth measurement for high brilliance laser beam sources using optical coherence tomography

As a result of the rapidly growing importance of applications in electro mobility that require a precisely defined laser weld depth, the demand for inline process monitoring and control is increasing. To overcome the challenges in process data acquisition, this paper proposes the application of a novel sensor concept for deep penetration laser beam welding with high brilliance laser sources. The experiments show that optical coherence tomography (OCT) can be used to measure the weld depth by comparing the distance to the material surface with the distance to the keyhole bottom measured by the sensor. Within the presented work, the measuring principle was used for the first time to observe a welding process with a highly focused laser beam source. First, a preliminary experimental study was carried out to evaluate the influence of the angle of incidence, the material, and the weld joint geometry on the quality of the sensor signal. When using a multimode fiber laser with a focus diameter of 320 μm, the measurements showed a distinct behavior for aluminum and copper. The findings about the measurement signal properties were then applied to laser beam welding with a single-mode fiber laser with a spot diameter of only 55 μm. The spot diameter of the OCT measuring beam was about 50 μm and thus only slightly smaller than that of the single-mode processing beam. A wide variety of tests were carried out to determine the limits of the measurement procedure. The results show that the application of OCT allows inline monitoring of the weld depth using both a multimode and a highly focused single-mode laser beam. In addition, various influences on the signal were identified, e.g., the material-specific melt pool dynamics as well as several characteristic reflection and absorption properties.