In-line Control Research Articles

Integration of Impedimetric Sensors for In Situ Electrochemical Impedance Spectroscopy in Free-Flow Electrophoresis Applications in Lab-on-Chip Systems.

Miniaturization and integration of chemical reactions into fluidic systems in combination with product purification or buffer exchange can reduce the amount of solvents and reactants required while increasing synthesis efficiency. A critical step is the regulation of flow rates to realize optimal synthesis conditions and high purification rates, so real-time, label-free monitoring is required in methods such as free-flow electrophoresis. Optical detection methods are widely used, but they often have complex excitation and detection setups that are disadvantageous for point-of-care applications. The method we have chosen is electrochemical impedance spectroscopy for detecting charged compounds in aqueous buffers with low ionic strength. Propranolol was selected for proof of concept and was separated from the organic solvent and the precursor oxirane by free-flow electrophoresis. For this purpose, electrode structures were fabricated in microfluidic channels by photolithographic lift-off technique and optimized in terms of positioning, electrode size and distance for sensitive detection, and quantification of propranolol in the nanomolar range. It is also noteworthy that the organic solvent dimethyl sulfoxide (DMSO) could be detected and quantified by an increased impedance magnitude. Subsequently, the optimized interdigital electrode structures were integrated into the outlet channels of the electrophoretic separation chamber to monitor the various outgoing fluidic streams and provide in-line control of the fluidic flows for the purification step. In conclusion, we can provide a microfluidic chip to monitor the separation efficiency of a substance mixture during free-flow electrophoresis without the need of complex analytical techniques using electrochemical impedance spectroscopy.

Inline tape width control for thermoplastic automated tape layup

Automated tape layup, is predisposed to positioning defects like gaps and overlaps which are detrimental to the structural integrity of the part. This work investigates the details of the design of inline width control concept, a technique for inline control of consolidated tape width to eliminate gaps. Continuous inline width variation is achieved by online control of the compaction force, during the layup process. Successful calibration and benchmarking tests using standard pressure measurement sensors are presented. Performance assessment tests are performed which demonstrate online variation of force from 50 N to 1000 N, having a standard deviation of 10 N. Correlation models for width control are obtained and it is shown that a width increase of 5–50 % of initial width is achievable using the prototype. An example of linearly varying force resulting in a trapezium shaped consolidated tape is also shown as a part of the feasibility study.

(Invited) Analytical Solutions for Quality Assurance across the Battery Manufacturing Process

Battery performance degradation and potentially dangerous incidents such as thermal runaway and fire can originate from defects in lithium battery components or during cell assembly. Therefore, quality assurance is extremely important aspect of the manufacturing process to identify and eliminate defects that may give rise to cell failure and dangerous incidents. From validating raw material purity and monitoring electrode thickness, to inspection of component alignment and integrity during assembly, different analytical capabilities are used to ensure quality is maintained through battery manufacturing workflow. In this presentation examples of using in-line and off-line analytical characterization techniques, such as in-line basis weight and thickness measurement systems and electron microscopes, to monitor the quality during battery manufacturing process will be discussed. Advances in in-line and off-line metrology and quality control tools are an important enabler for solving the current challenges and facilitating future growth in electric propulsion and energy storage technologies.

Learned Anomaly Detection with Terahertz Radiation in Inline Process Monitoring

Terahertz tomographic imaging as well as machine learning tasks represent two emerging fields in the area of nondestructive testing. Detecting outliers in measurements that are caused by defects is the main challenge in inline process monitoring. An efficient inline control enables to intervene directly during the manufacturing process and, consequently, to reduce product discard. We focus on plastics and ceramics, for which terahertz radiation is perfectly suited because of its characteristics, and propose a density based technique to automatically detect anomalies in the measured radiation data. The algorithm relies on a classification method based on machine learning. For a verification, supervised data are generated by a measuring system that approximates an inline process. The experimental results show that the use of terahertz radiation, combined with the classification algorithm, has great potential for a real inline manufacturing process. In a further investigation additional data are simulated to enlarge the data set, especially the variety of defects. We model the propagation of terahertz radiation by means of the Eikonal equation.

Generation of different mode-locked states in a Yb-doped fiber laser based on nonlinear multimode interference.

We demonstrated an ultrafast Yb-doped fiber laser with a single mode fiber-graded index multimode fiber-single mode fiber (SMF-GIMF-SMF) structure based saturable absorber. The GIMF was placed in the groove of an in-line fiber polarization controller to adjust its birefringence, enabling the SMF-GIMF-SMF structure to realize efficient saturable absorption based on nonlinear multimode interference without strict length restriction. By adjusting two intra-cavity polarization controllers, stable dissipation solitons and noise-like pulses were achieved in the 1030 nm waveband with pulse durations of 10.67 ps and 276 fs, respectively. We also realized Q-switched mode-locked pulses in the same fiber laser cavity. By the dispersive Fourier transform method, the real-time spectral evolution in the buildup process of the Q-switched mode-locked state was captured, which showed that the continuous-wave in this laser could gradually evolved into the stable Q-switched mode-locked pulses through unstable self-pulsation, relaxation oscillation and rogue Q-switching stage. To the best of our knowledge, our work reveals the buildup dynamics of the Q-switched mode-locked operation in a fiber laser for the first time. And we also studied the real-time spectral evolution of the stable Q-switched mode-locked pulses, which exhibited periodic breathing property.

In-Line Quality Control for Tempered Safety Glass

In-Line Quality Control for Tempered Safety Glass

Homogeneity Characterization of Textile-Integrated Wearable Sensors based on Impedance Spectroscopy.

One of the main challenges during the integration of a carbon/polymer-based nanocomposite sensor on textile substrates is the fabrication of a homogeneous surface of the nanocomposite-based thin films, which play a major role in the reproducibility of the sensor. Characterizations are therefore required in every fabrication step to control the quality of the material preparation, deposition, and curing. As a result, microcharacterization methods are more suitable for laboratory investigations, and electrical methods can be easily implemented for in situ characterization within the manufacturing process. In this paper, several textile-based pressure sensors are fabricated at an optimized concentration of 0.3 wt.% of multiwalledcarbon nanotubes (MWCNTs) composite material in PDMS. We propose to use impedance spectroscopy for the characterization of both of the resistive behavior and capacitive behavior of the sensor at several frequencies and under different loads from 50 g to 500 g. The impedance spectra are fitted to a model composed of a resistance in series with a parallel combination of resistance and a constant phase element (CPE). The results show that the printing parameters strongly influence the impedance behavior under different loads. The deviation of the model parameter of the CPE from the value 1 is strongly dependent on the nonhomogeneity of the sensor. Based on an impedance spectrum measurement followed by parameter extraction, the parameter can be determined to realize a novel method for homogeneity characterization and in-line quality control of textile-integrated wearable sensors during the manufacturing process.

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts.

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

A Novel Approach for Non-Invasive Continuous In-Line Control of Perfusion Cell Cultivations by Raman Spectroscopy.

Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.

Highly stable multi-wavelength fiber laser based on hybrid enclosed two-armed fiber filter

A compact multi-wavelength fiber laser (MWFL) with high stability based on hybrid enclosed two-armed fiber filter (HETAFF) is demonstrated. The HETAFF composes of an enclosed Mach-Zehnder interferometer with an in-line polarization controller (PC) in one arm of optical couplers #1 (OC1) and a section of polarization maintaining fiber with two PCs in OC2 loop. By using the HETAFF in proposed fiber laser, a lasing state switchable and wavelength-interval adjustable MWFL is obtained. It can be switched in single-, dual-, triple-, quadruple- and quintuple-wavelength states. The wavelength-interval can be adjusted as it operates in dual-, triple- and quadruple-wavelength states. Its optical signal to noise ratio (OSNR) is more than 36.03 dB, and the lasing wavelength shift and power fluctuation are less than 0.08 nm and 0.94 dB, respectively. The output characteristics, such as abundant laser states and high stability, are inseparable from the unique performance of the HETAFF.

Prediction of riboflavin and ascorbic acid concentrations in skimmed heat-treated milk using front-face fluorescence spectroscopy

The objective of this work was to obtain degradation kinetic models of riboflavin and ascorbic acid, and to evaluate the potential of front-face fluorescence (FFF) spectroscopy to predict their concentration in milk after thermal processing. A factorial design with three temperatures (70, 80 and 90 °C) and nine times (from 0 to 60 min) of heat treatment was used. Degradation kinetics of both vitamins were of first order. Predictive kinetic models using the Arrhenius equation had a variability coefficient of 2.26 and 3.64% for riboflavin and ascorbic acid, respectively. With fluorescent predictors such as tryptophan, Maillard compounds and riboflavin, prediction models with coefficients of variation smaller than 3.57% for riboflavin and ranging from 4.24 to 14.25% for ascorbic acid were obtained. In conclusion, FFF is a viable alternative to predict riboflavin and ascorbic acid content of milk which could allow in-line monitoring and control of thermal damage.

From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media

The modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO2 storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: the permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ∼100 CFD simulations.

Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review

Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future.

Latent cure epoxy resins for reliable joints in secondary-bonded composite structures

In high-performance polymer matrix composite assemblies, adhesive bonding is generally superior to mechanical fastening in structural performance and manufacturing efficiency. However, adhesive bonds are susceptible to minute levels of contamination accumulated during assembly that can lead to unpredictable, weak bonds. Current methods of measuring bond strength are all destructive mechanical tests. To overcome these challenges, redundant load paths (e.g., mechanical fasteners) are often implemented in secondary-bonded, primary-structures, which can greatly reduce structural performance. This study investigated reformulated aerospace epoxy matrix resins with stoichiometric offset to inhibit cure of the matrix resin prior to assembly. Inhibited resins can reflow and mix across the joint interface , which eliminates the material discontinuity and forms a homogenous joint with reliable fracture properties . The goal of this study was to develop and demonstrate secondary composite assemblies that are mechanically and microscopically indistinguishable from a co-cured composite joint. This article describes the development of latent epoxy resins, the fabrication of test articles, and the mechanical properties measured from experimental joints compared with conventional, co-cured laminates. Methods of in-line quality control using and infrared spectroscopy and post-assembly forensics are also described. The final mode-II fracture toughness measured from precracked AERoBOND specimens was similar to that measured from co-cured laminates indicating that later cure epoxy materials could be a suitable replacement for secondary bonding. • Methods enable rapid assembly of thermoset composites with the simplicity of secondary bonding and reliability of co-cure. • Epoxy based resins were formulated with offset stoichiometry: epoxy rich and hardener rich. • End-notched flexure tests indicated that joints with offset stoichiometry matched or exceeded co-cured joint performance. • Infrared spectroscopy and ultrasonic inspection were viable means of inline quality control before and after assembly.

Photoluminescence Imaging for the In-Line Quality Control of Thin-Film Solar Cells

Renewable energy sources such as photovoltaic (PV) technologies are considered to be key drivers towards climate neutrality. Thin-film PVs, and particularly copper indium gallium selenide (CIGS) technologies, will play a crucial role in the turnaround in energy policy due to their high efficiencies, high product flexibility, light weight, easy installation, lower labour-intensiveness, and lower carbon footprint when compared to silicon solar cells. Nonetheless, challenges regarding the CIGS fabrication process such as moderate reproducibility and process tolerance are still hindering a broad market penetration. Therefore, cost-efficient and easily implementable in-line process control methods are demanded that allow for identification and elimination of non-conformal cells at an early production step. As part of this work, a practical approach towards industrial in-line photoluminescence (PL) imaging as a contact-free quality inspection tool is presented. Performance parameters of 10 CIGS samples with 32 individually contacted cells each were correlated with results from PL imaging using green and red excitation light sources. The data analysis was fully automated using Python-based image processing, object detection, and non-linear regression modelling. Using the red excitation light source, the presented PL imaging and data processing approach allows for a quantitative assessment of the cell performance.

ZAero- Zero defect manufacturing of composite parts in the aerospace industry

During the manufacturing process of a carbon fiber (CFRP) part in the aircraft industry defects occur. These defects are identified through inspections carried out in intermediate steps and even at the end of the manufacturing process. Due to these defects reworks need to be performed and even futilities. The aims of this project is the inline control of the defects that will be produced along the manufacturing  of a stiffned surface panel. The ZAero control system consists on sensors integrated in the lay-up machine and sensors used during the infusion and resin curing processes. These sensors will detect defects that are outside the acceptance range and a response will occur. This response may be a rework as in the case of defects produced in the lamination stage or the variation of parameters produced in the infusion and resin curing stage due to the process monitoring. Three demonstrators will be manufactured. The degree of complexity will be increased in each demonstrator. The control system through sensors will make a quality control and when a defect that is outside the range of acceptance a response will occur.

Model development for the design of control strategies of the primary drying of lyophilization in vials

This paper proposes a development to reformulate the fundamental equations of primary drying into a linear-based representation to support control design. The model is expressed by transfer functions with variable gains that incorporate analytical relations derived from a phenomenological model. The time constants denote the dynamics of the heating/cooling and vacuum systems. Compared to fundamental equations, this approach simplifies the development and implementation of well-established control algorithms. The quality of the proposed representation is illustrated in the design of a control system using two different strategies: model predictive control and a feedback controller with proportional-integral action. The simulation case study allows assessing the performance of both designs with regard to cycle time reduction and robustness under parametric disturbances. Results evidence that the proposed model is detailed enough to provide accuracy in a simplified way, facilitating the implementation of in-line control strategies, which in turn enable significant reductions of drying time.

Towards real-time release of pharmaceutical tablets: 100% in-line control via near-infrared spatially resolved spectroscopy and 3D microwave resonance technology

In the scope of 100% in-line quality control and real-time release of pharmaceutical tablets, the authors present a flexible inspection module for in-line tablet analysis with integrated multipoint near-infrared (NIR) spectroscopy and 3D microwave resonance technology (3D MRT). Via an industrial case study on Diclofenac Sodium tablets, the abilities of this versatile process analytical technology (PAT) tool are presented. It is demonstrated that the combination of Diclofenac concentration prediction via NIR spectroscopy and mass prediction via 3D MRT allow to estimate the dosage of each individual tablet. Single sample repetition tests were performed on 5 tablets, measured 10 times on three different days. A high accuracy and precision of prediction was shown, with an average standard deviation below 0.5 mg. The inspection run demonstrated the added value of such inspection and sorting strategies based on the calculated dosage of individual tablets.

(Invited) High-Throughput, In-line Quality Control of Fuel Cell Materials

Electrochemical energy conversion technologies are actively being commercialized for large-scale applications, such as zero-pollution automotive systems. There has been significant progress in pioneering research, such as the discovery and development of new membranes, catalysts, gas diffusion layers, membrane electrode assemblies, and operating conditions. These technologies need to make the jump from laboratory research-scale to robust production-scale. In many cases, scale up requires compromises on materials, methodology, and quality control of each component. Current manufacturing practices generally rely heavily on manual, visual inspection of individual components followed by cell testing to indicate material issues, which leads to delayed correction of production issues and added cost in the form of additional labor and expensive material loss. Reducing the extent of the manual labor and compromises during scale up requires addition and integration of in-line, real-time, quality control systems that provide feedback for process correction.Mainstream Engineering has developed, built and evaluated an in-line optical scanner based inspection system with the capability to determine membrane thickness, identify defects, and measure catalyst variability and loading. The inspection system samples the entire surface so that defects in the materials can be identified and selectively removed prior to assembly. We have worked with our partners to characterize a wide range of membranes and catalysts types using off-line techniques including x-ray fluorescence, ion-coupled plasma optical emission spectroscopy, Raman spectroscopy, and IR thermography to identify defects and their impact on cell performance. This data was then used to build calibrations and develop in-line, automated detection algorithms that can be used during full-scale production of materials, to quantify and isolate defects at multiple stages of the production process. We have used our in-line system to characterize a variety of materials including proton exchange membrane, anion exchange membrane, fluorinated and hydrocarbon membranes, catalyst coated membranes, gas diffusion layers, and gas diffusion electrodes in a variety of configurations and chemistries. This process can be rapidly adapted and applied to new materials and processing approaches as they are developed in the future order to help reduce development time in, and transition from research to production systems, as well as providing high-throughput, real-time diagnostic capability for commercial applications.