In-line Control Research Articles

Optimizing Epoxy Molding Compound Processing: A Multi-Sensor Approach to Enhance Material Characterization and Process Reliability.

The in-line control of curing during the molding process significantly improves product quality and ensures the reliability of packaging materials with the required thermo-mechanical and adhesion properties. The choice of the morphological and thermo-mechanical properties of the molded material, and the accuracy of their determination through carefully selected thermo-analytical methods, play a crucial role in the qualitative prediction of trends in packaging product properties as process parameters are varied. This work aimed to verify the quality of the models and their validation using a highly filled molding resin with an identical chemical composition but 10 wt% difference in silica particles (SPs). Morphological and mechanical material properties were determined by dielectric analysis (DEA), differential scanning calorimetry (DSC), warpage analysis and dynamic mechanical analysis (DMA). The effects of temperature and injection speed on the morphological properties were analyzed through the design of experiments (DoE) and illustrated by response surface plots. A comprehensive approach to monitor the evolution of ionic viscosity (IV), residual enthalpy (dHrest), glass transition temperature (Tg), and storage modulus (E) as a function of the transfer-mold process parameters and post-mold-cure (PMC) conditions of the material was established. The reliability of Tg estimation was tested using two methods: warpage analysis and DMA. The noticeable deterioration in the quality of the analytical signal for highly filled materials at high cure rates is discussed. Controlling the temperature by increasing the injection speed leads to the formation of a polymer network with a lower Tg and an increased storage modulus, indicating a lower density and a more heterogeneous structure due to the high heating rate and shear heating effect.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Lower limb squat biomechanics and select clinical measures in chronic ankle instability

BackgroundIndividuals with chronic ankle instability often present with clinical and biomechanical abnormalities, however squat biomechanics have not been investigated. The purpose of this study was to compare select clinical assessments and squat biomechanics between individuals with and without chronic ankle instability. MethodsFifteen individuals with chronic ankle instability and a matched control group were studied. A weight-bearing dorsiflexion lunge test, foot posture, and an in-line half-kneeling motor control test for core stability were assessed. Lower limb 3D bilateral and unilateral squat biomechanics were captured. Groups, limbs and squat tasks were compared using an alpha of 0.05. FindingsIndividuals with chronic ankle instability had less static weight-bearing dorsiflexion and failed the core stability test more frequently, but did not differ in foot morphology compared to the controls. When squatting, those with chronic ankle instability demonstrated reduced peak ankle dorsiflexion angles and moments in the involved limb (p < 0.04) during single limb squats and had interlimb differences in ankle dorsiflexion angle as well as hip and knee kinetics (p < 0.04) during double limb squats. In those with chronic ankle instability, there was less overall motion, but higher kinetic demands in single limb versus double leg squatting (p < 0.03). InterpretationIndividuals with chronic ankle instability had impaired weight-bearing dorsiflexion and showed impaired core stability more often, which accompanied altered squatting mechanics in both variations. Within the limbs with chronic ankle instability, single limb squats showed lesser kinematic demands but higher kinetic demands than double limb squatting.

Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

X-ray computed tomography (XCT) is a validated and frequently used tool to verify part geometry and to perform a non-destructive inspection of additive manufacturing parts. However, the acquisition of a large number of x-ray projections generates long inspection times. This conflicts with a high throughput of the production process and hinders the integration of XCT as an in-line quality control procedure for low-end parts. In this paper, we propose a method to obtain three-dimensional (3D) information of the internal pores from a limited view or limited angle scan. The method combines a forward projection model of a cone-beam x-ray system and a deep learning neural network to directly classify each individual voxel, based on the x-ray projections in order to avoid the reconstruction and segmentation step. Accompanying reconstruction artifacts for limited view and limited angle XCT scans are thereby reduced, while preserving 3D information of the pores, defects or inclusions inside the material. The method is validated on real x-ray projections of polymer laser sintered industrial parts and shows a significant reduction in the required x-ray projections, hence acquisition time.

Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

AbstractThis study describes the systematic process of training, testing, and validating a soft sensor designed for quality control of a turning process on components made of AISI 4140 steel. The soft sensor allows product quality to be predicted and unfavorable surface conditions to be identified, in particular the appearance of a phenomenon known as “White Layer”, often characterized in the case of AISI 4140 steel by an ultra-fine-grained microstructure (UFG). Basis of the soft sensor is a data fusion supported by non-destructive testing techniques (NDT), particularly micromagnetic methods (3MA). A critical part of this work is to address challenges such as lift-off compensation and in-process detection using 3MA. The application of machine-learning techniques, including Principal Component Analysis (PCA) and regression analysis, is detailed. These techniques result in robust models capable of detecting the occurrence of the White Layer phenomenon while minimizing the influence of measurement setup variations and process disturbances. In addition, the study demonstrates the integration of NDT into the machining process which drives the soft sensor and allows suitable adjustments of the process parameters. The data-driven soft sensor approach demonstrates a possible In-Line control system and discusses different control theories and their respective advantages and disadvantages. This system can effectively set targeted surface conditions in real time during the turning process.

All-fiber Q-switched erbium-doped fiber laser generation with SMF-SIMF-SMF structure as artificial saturable absorber

A passively Q-switched Erbium-doped fiber laser (EDFL) was successfully demonstrated using SMF (single-mode fiber)-SIMF (step-index multimode fiber)-SMF or SMS structure as a saturable absorber (SA). The SMS structure operates on the Kerr-effect of multimode interference (MMI). To modulate the cavity loss, a 158 mm length of SIMF was fusion spliced into a ring cavity. By properly adjusting the in-line polarization controller (PC) that holds the SIMF, we were able to generate steady Q-switched pulses. As the input pump power increased from 115 to 343 mW, the repetition rate of the Q-switched laser increased from 87 to 158 kHz, while the corresponding pulse width decreased from 4.2 µs to 2.5 µs. The signal-to-noise ratio (SNR) of the fundamental mode for the pulsed laser was around 72 dB at a central wavelength of 1530 nm. Our results indicate that the SMS structure can be used as an SA to generate stable Q-switched EDFL with very simple cavity settings.

Optimizing solar cell metallization by parallel dispensing

Dispensing of metal pastes shows impressive performance and promises higher throughput with lower metal laydown than screen printing. Given the market introduction planned for latest 2024, and with the objective to fully automate the metallization process, an in-line visual control and inspection unit is being developed. Utilizing this tool, process time and paste laydown was reduced by another 10 % and 20 % respectively, after optimization within four consecutive cells only. Consequently, recent cell results on industrial precursor SHJ showed an increased efficiency by +0.4 %_"abs“ while having a reduction in paste usage of 40 %, compared to the screen printing reference. In this paper, we outline the path of development with a focus on first results with this new digital tool that has the potential to lift future PV metallization to a completely automated production state with higher performance, yield values and lower downtimes.

Development of a Calibration Model for Real-Time Solute Concentration Monitoring during Crystallization of Ceritinib Using Raman Spectroscopy and In-Line Process Microscopy

Raman spectroscopy is a useful tool for polymorphic form-monitoring during the crystallization process. However, its application to solute concentration estimation in two-phase systems like crystallization is rare, as the Raman signal is influenced by various changing factors in the crystallization process. The development of a robust calibration model that covers all variations is complex and represents a major challenge for the implementation of Raman spectroscopy for in-line monitoring and control of the solution crystallization process. This paper describes the development of a Raman-based calibration model for estimating the solute concentration of the active pharmaceutical ingredient ceritinib. Several different calibration approaches were tested, which included both temperature and spectra of clear solutions and slurries/suspensions. It was found that the concentration of the ceritinib solution could not be accurately predicted when suspended crystals were present. To overcome this challenge, the approach was enhanced by including additional variables related to crystal size and solid concentration obtained via in-line process microscopy (chord-length distribution percentiles D10, D50 and D90) and turbidity. Partial least squares regression (PLSR) and artificial neural network (ANN) models were developed and compared based on root mean square error (RMSE). ANN models estimated the solute concentration with high accuracy, with the prediction error not exceeding 1% of the nominal solute concentration.

Mechanochemical Degradation of Biopolymers.

Mechanochemical treatment of various organic molecules is an emerging technology of green processes in biofuel, fine chemicals, or food production. Many biopolymers are involved in isolating, derivating, or modifying molecules of natural origin. Mechanochemistry provides a powerful tool to achieve these goals, but the unintentional modification of biopolymers by mechanochemical manipulation is not always obvious or even detectable. Although modeling molecular changes caused by mechanical stresses in cavitation and grinding processes is feasible in small model compounds, simulation of extrusion processes primarily relies on phenomenological approaches that allow only tool- and material-specific conclusions. The development of analytical and computational techniques allows for the inline and real-time control of parameters in various mechanochemical processes. Using artificial intelligence to analyze process parameters and product characteristics can significantly improve production optimization. We aim to review the processes and consequences of possible chemical, physicochemical, and structural changes.

Towards In-line Control of Continuous Resistance Welding for Joining Structural Thermoplastic Composites

The continuous resistance welding (CRW) process consists of an end-effector which moves along the length of a weld seam, heating a conductive implant while compacting the joint locally throughout the melt and solidification stages of the thermoplastic material. The performance of the joint has been shown to be highly dependent on the process temperature at the weld interface; however, this cannot be measured directly during the process in a non-invasive manner. Other parameters such as boundary conditions, substructure properties, or part geometry may vary along the length of the weld. As such, a physics-based simulation is developed founded upon an “MSTEP” framework which defines how the materials (M), shape (S), tooling (T), and equipment (E) interact to determine the process (P). Detailed finite element (FE) models are developed for thermal analysis based on the weld geometry, boundary conditions, and previously developed and validated melt/crystallization models for the thermoplastic matrix. Experimental CRW tests are presented to validate simulations and calibrate suitable control variables.

Evaluation of shelf life of egg treated with edible coating by means of NIR spectroscopy and laser induced diffuse reflectance imaging

A coating solution was developed by blending cassava starch at different levels (2%, 3% and 4% w/v) with gelatin and plasticizers to maintain the quality of eggs during 4 weeks of storage (25 ± 1 °C, 60–65% RH). This study suggests the rapid techniques of near-infrared (NIR) spectroscopy and laser-induced diffuse reflectance imaging for determining the internal quality of eggs. The scanning electron microscopy (SEM) micrographs showed that the coatings were effective in protecting eggshells. During storage, coated eggs had lower weight loss and albumen pH, higher Haugh unit and yolk index than control eggs (p < 0.05). These factors are the most common parameters in assessing egg quality. Among the coating formulas, the 4% starch coating had significantly higher effectiveness from the 3rd week of storage. The results indicated that the coating solution containing 4% starch could maintain the egg quality up to 28 days. Furthermore, the NIR spectra were used to develop prediction models for the investigated quality attributes using partial least squares regression (PLSR). Models were created using 3 preprocessing methods of standard normal variate (SNV), first derivative (lD) and second derivative (2D). The PLSR-SNV model achieved the best prediction for weight loss (R2 = 0.883, RPD = 2.920), Haugh unit (R2 = 0.756, RPD = 2.025), and yolk index (R2 = 0.807, RPD = 2.276). Laser scattering was successfully used to detect the egg quality change during storage by linear discriminant analysis (LDA) at wavelengths of 532 nm and 780 nm. The descriptive attributes extracted from intensity profiles showed significant effects of storage time, treatment and their interaction. This research highlighted the promising application of NIR and laser-induced diffuse reflectance imaging for inline control of egg quality at room temperature.

Accurate Thickness Measurement Based on Dispersion Compensation via Terahertz Time-Domain Spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) has shown significant potential in thickness detection. The time-of-flight (TOF) method is most widely used for judgment. However, the method only refers to peak information, and waveform information, such as deformation, is simply ignored. Consequently, errors are introduced when testing samples with high dispersion and large absorptive properties. As a result, the signals are broadened, and the peaks become smooth or even disappear, which makes the TOF method fail. In this article, we propose a dispersion compensation (DC) algorithm to solve the above problems. Thickness can be accurately determined by nondispersive signals in the spatial domain. Amplitude compensation is used for materials with large absorption. The DC algorithm’s error is less than 1% of the TOF algorithm’s error in simulations. While measuring LiNbO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{3}$</tex-math> </inline-formula> and water film, errors are, respectively, reduced by 96.24% and 50% compared with using the TOF method, and corresponding deviations are 0.2% and 1.6% compared with the reference thickness. Moreover, the DC algorithm is noniterative, stable, and requires much less calculations than the fitting method. The DC method drastically improves the accuracy by overcoming inherent drawbacks of the TOF method. It can be applied in fields such as nondestructive testing and in-line quality control because of noncontact and real-time advantages.

Computer vision and machine learning for assessing dispersion quality in carbon nanotube / resin systems

The addition of nanomaterials to polymeric resins can enhance a range of bulk material properties, but the nanofiller effectiveness varies strongly on the dispersion quality. The ability to independently, objectively, and quickly assess the dispersion quality of nano-loaded resins based on microscopy is desirable, but current techniques are often subjective and time-consuming. For this paper, we utilize a dispersion metric based on the use of image segmentation of optical microscope images. We then show that by training a computer vision model on a dataset of segmented microscopy images, the model can then quickly and accurately assess the dispersion of nanoparticles in a material. We apply this process to microscope images of carbon nanotube-loaded commercial resins. Our results indicate that this machine-learning methodology can match the accuracy and repeatability of current methods. In principle, this same machine-learning approach can be applied to a broad range of nanomaterials and matrices, allowing for rapid and quantitative analysis of microscope images for in-line quality control.

Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging

Large area manufacturing processes of thin films such as large-area vacuum roll-to-roll coating of dielectric and gas permeation barrier layers in industry require a precise control of e.g. film thickness, homogeneity, chemical compositions, crystallinity and surface roughness. In order to determine these properties in real time, hyperspectral imaging is a novel, cost-efficient, and fast tool as in-line technology for large-area quality control. We demonstrate the application of hyperspectral imaging to characterize the thickness of thin films of the multilayer system ZTO/Ag/ITO produced by roll-to-roll magnetron sputtering on 220 mm wide polyethylene terephthalate substrate. X-ray reflectivity measurements are used to determine the thickness gradients of roll-to-roll produced foils with sub nanometer accuracy that serve as ground truth data to train a machine learning model for the interpretation of the hyperspectral imaging spectra. Based on the model, the sub-layer thicknesses on the complete substrate foil area were predicted which demonstrates the capabilities of this approach for large-scale in-line real-time quality control for industrial applications.

Evaluation of a PAT-based in-line control system for a continuous spin freeze-drying process

Continuous spin freeze-drying provides a range of opportunities regarding the implementation of several in-line process analytical technologies (PAT) to control and optimize the freeze-drying process at the individual vial level. In this work, two methods were developed to (1) control the freezing phase by separately controlling the cooling and freezing rate and (2) control the drying phase by controlling the vial temperature (and hence the product temperature) to setpoint values and monitoring the residual moisture content. During the freezing phase, the vial temperature closely followed the decreasing setpoint temperature during the cooling phases, and the crystallization phase was reproducibly controlled by regulating the freezing rate. During both primary and secondary drying, vial temperature could be maintained on the setpoint temperature which resulted in an elegant cake structure after every run. By being able to accurately control the freezing rate and the vial temperature, a homogeneous drying time (SD = 0.07–0.09 h) between replicates was obtained. Applying a higher freezing rate significantly increased primary drying time. On the other hand, fast freezing rates increased the desorption rate. Finally, the residual moisture of the freeze-dried formulation could be monitored in-line with a high accuracy providing insight on the required length of the secondary drying phase.

Experimental validation of multi-vial control for primary drying in a pilot-scale unit

Primary drying is the bottleneck of pharmaceutical lyophilization, as it represents the most time and energy-consuming stage and largely determines the quality of the final product. The common practice runs the process under pre-calculated input profiles while considering average drying dynamics for the batch. This results in conservative and underperforming cycles. In this regard, the paper proposes and experimentally validates an in-line control strategy for primary drying. It explicitly accounts for intra-lot drying heterogeneity using three models, each one calibrated for a reference vial position in the chamber. Two separate experiments evaluate the proposed framework: offline and in-line process control. Validation on a pilot-scale unit shows a cycle time reduction of the proposed approaches compared to standard drying protocol at constant operating conditions. The input profiles obtained by offline control lead to a 16% shorter cycle time; however, the lack of feedback during the cycle impedes addressing potential parametric variations and model mismatches. Conversely, in-line control yields a reduction of 13% while allowing careful monitoring of the product status, ensuring a more dependable cycle. In both cases, the visual quality inspection confirms the acceptability of the final product without visible degradation.

Development of a dual-electrospray ionization source with in-line absorbance-based voltage control.

Emitter tip arrays for electrospray ionization have been used for a variety of MS sample introduction purposes, including detection of multiple sample eluent streams and improved accuracy through parallel infusion of an internal standard. User control is typically required for targeted application of high voltage to specific channels to maximize analyte signal and minimize other background signals. In this communication, an automated approach to applying electrospray voltage only when a detectable analyte is present is described. An in-line absorbance detector is used to identify the presence of an analyte in the fluidic path between the sample introduction valve and the mass spectrometer. A Raspberry Pi-controlled system is then used to apply high voltage to a downstream emitter tip at the MS inlet following a delay volume between the detectors. Demonstration of this technique on two parallel sample channels is reported, including a pulsed voltage application to maximize signal when analytes elute on each channel simultaneously.

Determining material model parameters by optimization for temperature controlled friction stir additive manufacturing

Friction stir additive manufacturing (FSAM) is a novel process with the potential of being used for efficiently fabricating large aircraft structures. It is based on the repeated stacking and friction stir lap welding of metal sheets. Recent studies have reported that temperature-related defects occur in the upper layers of the builds, which are explained by changed thermal conditions. The temperatures during fabrication can be calculated by a digital twin of the process. A precise material model is necessary for this purpose. This paper presents and discusses several strategies used to determine Johnson-Cook material model parameters for the aerospace aluminum alloy EN AW-7075. Using only the process data of two welding experiments, suitable material model parameters (C of -0.14161, m of 0.66863) were determined by solving an optimization problem. For these parameters, the mean absolute temperature error was less than or equal to 4.07 K for unknown test data. The method therefore enables the inline calculation and control of the welding temperature during FSAM, thus ensuring stable thermal conditions and high-quality parts.

Liposome manufacturing under continuous flow conditions: towards a fully integrated set-up with in-line control of critical quality attributes.

Continuous flow manufacturing (CFM) has shown remarkable advantages in the industrial-scale production of drug-loaded nanomedicines, including mRNA-based COVID-19 vaccines. Thus far, CFM research in nanomedicine has mainly focused on the initial particle formation step, while post-formation production steps are hardly ever integrated. The opportunity to implement in-line quality control of critical quality attributes merits closer investigation. Here, we designed and tested a CFM setup for the manufacturing of liposomal nanomedicines that can potentially encompass all manufacturing steps in an end-to-end system. Our main aim was to elucidate the key composition and process parameters that affect the physicochemical characteristics of the liposomes. Total flow rate, lipid concentration and residence time of the liposomes in a high ethanol environment (i.e., above 20% v/v) emerged as critical parameters to tailor liposome size between 80 and 150 nm. After liposome formation, the pressure and the surface area of the filter in the ultrafiltration unit were critical parameters in the process of clearing the dispersion from residual ethanol. As a final step, we integrated in-line measurement of liposome size and residual ethanol content. Such in-line measurements allow for real-time monitoring and in-process adjustment of key composition and process parameters.