In the manufacturing process of float glass often atmospheric pressure chemical vapour deposition (APCVD) reactors are integrated on-line for the deposition of functional thin solid films. Such functional films have applications in architectural glass, flat panel displays and solar cells. As glass moves downstream in the process, the thin film is deposited at temperatures between 500 to 700°C. The high temperatures make it difficult to monitor the deposition process and thin film quality control is commonly done at the end of the line or at lower temperatures. A time delay therefore exists between the point of thin film deposition and subsequent quality control, which can lead to large quantities of defective product being produced before faults are detected. It is therefore desirable to monitor in the APCVD reactor for rapid feedback of unexpected deviations from desired process conditions, reaction progress and fault detection. High uniformity of film properties across the substrate are important, but APCVD reactors are often empirically designed and the detailed chemical reaction mechanism is unknown. This leads to inefficient gas flow patterns and precursor utilization as well as difficulties in the design of new reactors. The APCVD deposition of tin oxide from the mono-butyl-tin tri-chloride (MBTC) is an example of such a process. Optical monitoring instruments in-situ and in-line on the APCVD reactor provided rapid feedback about process stability and progress non-invasively. Near infrared diode laser absorption spectroscopy (NIR-LAS) monitored the concentration of the reaction species hydrogen chloride (HCl) in-situ and spatially in the coating zone. A mid-infrared grating absorption spectrometer (IR-GAS) with novel pyro-electric array detector monitored the concentration of precursor entering the coating system simultaneously. In combination these instruments provide the means for rapid process feedback. Fourier transform infrared absorption spectroscopy (FTIR) was used to investigate the unknown decomposition pathway of the precursor to find the yet unknown key tin radical that initiates film growth. Stable species forming during MBTC decomposition over a temperature range of 170 to 760°C were investigated but the tin intermediate remains unknown. Computational fluid dynamics (CFD) is routinely employed in research and industry for the numerical simulation of CVD processes in order to predict reactor flow patterns, deposition rates, chemical species distribution or temperature profiles. Two and three dimensional models with complex geometries and detailed reaction models exist. A three dimensional computational fluid dynamics (CFD) model of the used APCVD reactor was built using the Fluent CFD software. The numerical simulation included a chemical model that predicted qualitatively the chemical species distribution of hydrogen chloride in the gas phase. This was confirmed through comparison with NIR-LAS results. Design shortcomings due to inefficient flow patterns were also identified. In combination the optical tools developed provide the means for safe and efficient manufacturing of thin films in APCVD reactors. CFD simulations can be used to increase precursor utilization and film uniformity in the development of new reactor designs.

[EN] An accurate fault diagnosis of both, faults sensors and real process faults have become more and more important for process monitoring (minimize downtime, increase safety of plant operation and reduce the manufacturing cost). Quick and correct fault diagnosis is required in order to put back on track our processes or products before safety or quality can be compromised. In the study and comparison of the fault diagnosis methodologies, this thesis distinguishes between two different scenarios, methods for multivariate statistical quality control (MSQC) and methods for latent-based multivariate statistical process control: (Lb-MSPC). In the first part of the thesis the state of the art on fault diagnosis and identification (FDI) is introduced. The second part of the thesis is devoted to the fault diagnosis in multivariate statistical quality control (MSQC). The rationale of the most extended methods for fault diagnosis in supervised scenarios, the requirements for their implementation, their strong points and their drawbacks and relationships are discussed. The performance of the methods is compared using different performance indices in two different process data sets and simulations. New variants and methods to improve the diagnosis performance in MSQC are also proposed. The third part of the thesis is devoted to the fault diagnosis in latent-based multivariate statistical process control (Lb-MSPC). The rationale of the most extended methods for fault diagnosis in supervised Lb-MSPC is described and one of our proposals, the Fingerprints contribution plots (FCP) is introduced. Finally the thesis presents and compare the performance results of these diagnosis methods in Lb-MSPC. The diagnosis results in two process data sets are compared using a new strategy based in the use of the overall sensitivity and specificity / [ES] La realización de un diagnóstico preciso de los fallos, tanto si se trata de fallos de sensores como si se trata de fallos de procesos, ha llegado a ser algo de vital importancia en la monitorización de procesos (reduce las paradas de planta, incrementa la seguridad de la operación en planta y reduce los costes de producción). Se requieren diagnósticos rápidos y correctos si se quiere poder recuperar los procesos o productos antes de que la seguridad o la calidad de los mismos se pueda ver comprometida. En el estudio de las diferentes metodologías para el diagnóstico de fallos esta tesis distingue dos escenarios diferentes, métodos para el control de estadístico multivariante de la calidad (MSQC) y métodos para el control estadístico de procesos basados en el uso de variables latentes (Lb-MSPC). En la primera parte de esta tesis se introduce el estado del arte sobre el diagnóstico e identificación de fallos (FDI). La segunda parte de la tesis está centrada en el estudio del diagnóstico de fallos en control estadístico multivariante de la calidad. Se describen los fundamentos de los métodos más extendidos para el diagnóstico en escenarios supervisados, sus requerimientos para su implementación sus puntos fuertes y débiles y sus posibles relaciones. Los resultados de diagnóstico de los métodos es comparado usando diferentes índices sobre los datos procedentes de dos procesos reales y de diferentes simulaciones. En la tesis se proponen nuevas variantes que tratan de mejorar los resultados obtenidos en MSQC. La tercera parte de la tesis está dedicada al diagnóstico de fallos en control estadístico multivariante de procesos basados en el uso de modelos de variables latentes (Lb-MSPC). Se describe los fundamentos de los métodos mas extendidos en el diagnóstico de fallos en Lb-MSPC supervisado y se introduce una de nuestras propuestas, el fingerprint contribution plot (FCP). Finalmente la tesis presenta y compara los resultados de diagnóstico de los métodos propuestos en Lb-MSPC. Los resultados son comparados sobre los datos de dos procesos usando una nueva estrategia basada en el uso de la sensitividad y especificidad promedia. / [CAT] La realització d'un diagnòstic precís de les fallades, tant si es tracta de fallades de sensors com si es tracta de fallades de processos, ha arribat a ser de vital importància en la monitorització de processos (reduïx les parades de planta, incrementa la seguretat de l'operació en planta i reduïx els costos de producció) . Es requerixen diagnòstics ràpids i correctes si es vol poder recuperar els processos o productes abans de que la seguretat o la qualitat dels mateixos es puga veure compromesa. En l'estudi de les diferents metodologies per al diagnòstic de fallades esta tesi distingix dos escenaris diferents, mètodes per al control estadístic multivariant de la qualitat (MSQC) i l mètodes per al control estadístic de processos basats en l'ús de variables latents (Lb-MSPC). En la primera part d'esta tesi s'introduïx l'estat de l'art sobre el diagnòstic i identificació de fallades (FDI). La segona part de la tesi està centrada en l'estudi del diagnòstic de fallades en control estadístic multivariant de la qualitat. Es descriuen els fonaments dels mètodes més estesos per al diagnòstic en escenaris supervisats, els seus requeriments per a la seua implementació els seus punts forts i febles i les seues possibles relacions. Els resultats de diagnòstic dels mètodes és comparat utilitzant diferents índexs sobre les dades procedents de dos processos reals i de diferents simulacions. En la tesi es proposen noves variants que tracten de millorar els resultats obtinguts en MSQC. La tercera part de la tesi està dedicada al diagnòstic de fallades en control estadístic multivariant de processos basat en l'ús de models de variables latents (Lb-MSPC). Es descriu els fonaments dels mètodes més estesos en el diagnòstic de fallades en MSPC supervisat i s'introdueix una nova proposta, el fingerprint contribution plot (FCP). Finalment la tesi presenta i compara els resultats de diagnòstic dels mètodes proposats en MSPC. Els resultats són comparats sobre les dades de dos processos utilitzant una nova estratègia basada en l'ús de la sensibilitat i especificitat mitjana. / Vidal Puig, S. (2016). FAULT DIAGNOSIS TOOLS IN MULTIVARIATE STATISTICAL PROCESS AND QUALITY CONTROL [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61292 / TESIS

This diploma thesis deals with the proposal of the Supervisory Center and its subsequent implementation in an environment that supervises the specific activities in the IT environment.

No description available.

No description available.

In stamping operations, sheet metal is formed into a desired shape by pressing it in a hydraulic or mechanical press between suitably shaped dies. As a predominant manufacturing process, sheet metal forming has been widely used for the production of automobiles, aircraft, home appliances, beverage cans and many other industrial and commercial products. A major effort till date on stamping processes monitoring has been focused on investigating variations in the press force. Given that the press force itself is an integral of the contact pressure distribution over the die and binder contact interfaces, it is conceivable that defects may be better identified by analyzing the contact pressure distribution directly at the tooling-workpiece interface, instead of measuring the press force, which is less reflective of the localized forming process due to its nature as a secondary effect. It is thus desirable that a new, integrated sensing method capable of directly assimilating forming pressure distribution in the tooling structure be devised for improved stamping process monitoring. Designing such a distributed sensing scheme and analyzing the feasibility of its structural integration into a stamping tooling structure is the objective of this reported work. In this context, four research tasks have been identified and examined during the course of this work: 1) Devising a New, Embedded Sensing Method The new sensing method monitors stamping processes by means of an array of force sensors structurally integrated into the stamping tooling. The ability to directly measure local forming events by means of such an integrated and distributed sensing provides a new means of performing defect detection and process monitoring. Such a distributed sensing system overcomes the limitations of traditional tonnage and acceleration sensing systems which are focused on the measurement of indirect, global parameters. The new method is based on the evaluation of spatially continuous pressure surfaces from spatially discrete sensor measurements that are directly related to the local events at the stamping interface. To evaluate the effectiveness of this method, a panel stamping test bed equipped with an array of embedded force sensors has been designed, modeled and fabricated. Data obtained from experiments conducted on the test bed indicates that the new sensing method can be highly effective in process monitoring of stamping operations. 2) Reconstruction of Spatio-Temporal Distribution of Contact Pressure Structurally integrating sensors under tooling surfaces reduces the surface rigidity of the tool, thus limiting the number of sensors and the locations at which they can be embedded. This in turn affects the reconstruction of contact pressure distribution on the tooling surface. Numeric surface generation methods, such as Bezier surfaces and Thin Plate Spline surfaces offer a method for estimating the contact pressure distributions on the tooling surfaces from a sparse distribution of sensors. The concept of interpolating force distributions using surfaces has been investigated by researchers previously. However, selection of the surface generation method has remained largely an ad hoc process. The work presented here addresses this issue by using tooling interface contact pressure distribution information obtained from FE simulations as the basis for evaluating the accuracy of two commonly employed surface methods mentioned above. In order to reach a generic conclusion, the mathematical background of these schemes has been examined in light of the purpose at hand. The results indicate that an interpolative scheme such as the Thin Plate Spline surfaces (TPS), which can estimate the contact pressure distributions more accurately in a multi-sensor environment. The local and global accuracies of the Thin Plate Spline surface modeling technique have been experimentally evaluated using a sensor embedded stamping test bed designed for the purpose. 3) Modeling of Contact Pressure Distribution at the Sheet Metal-Tooling Interface Information about the contact pressure distribution at the tooling interface is critical to identifying the accuracy of numeric schemes that estimate by interpolation or approximation the contact pressure at any point on the tooling surface, based on a limited number of spatially distributed sensors. Furthermore, such knowledge is valuable in identifying operational parameters for the sensors to be integrated into the stamping tooling structure. In the absence of a tractable analytic method of determining the contact pressure distribution on stamping tooling surfaces, Finite Element models of a stamping operation have been created. Furthermore the drilling of sensor cavities under the working surfaces of the dies adversely affects the working life of stamping dies and their strength. The accuracy of analytic fatigue failure mechanics in evaluating the effect of parameters, such as embedding depth and sensor rigidity, on the operational life of the die, suffers from uncertainty in the estimation of stress concentrations around sharp geometric features of the sensor cavity. This shortcoming has been circumvented by the creation of FE models of the sensor cavity for more accurate estimation of stress concentrations around sharp geometries. The effect of different embedding materials on the sensitivity of embedded sensors has also been evaluated based on these models. 4) Defect Detection in Stamping Operation The ultimate goal of this thesis research was to study the feasibility of identifying defects in a stamping process based on the contact pressure distribution surfaces. This was achieved in this reported work by spatio-temporal decomposition of ‘parameters’ derived from the contact pressure distribution surfaces. Here ‘parameters’ refers to quantities such as the minimum, maximum, and mean contact pressures. These parameters have a time-varying spatial location as well as magnitude value associated with them. The feasibility of defect detection in stamping operations based on such parameters has been investigated. In addition to these focal areas, the design and implementation of a stamping test bed equipped for distributed contact pressure sensing has also been researched. This test bed was utilized for experimental verification of the developed theories and numerical models. Design of the proposed test bed required research into additional topics like the design of a protective package for embedded sensors and the effect of sensor embedding depth on contact pressure measurements. These issues have been addressed in this work, culminating in the experimental demonstration of the embedded pressure sensing system for process monitoring in the sheet metal stamping processes.

Hybrit development AB är ett bolag som strävar mot att kunna producera fossilfritt stål genom att reducera järnmalmspellets med hjälp av vätgas. Därför har Hybrit utfört experimentella kampanjer där genomförbarheten av att reducera järnmalmspellets med hjälp av vätgas undersökts och studerats. Vid produktion av järn och stål måste produktkvalitén tas i beaktan. Reduktionsprocessen karaktäriseras av en mängd olika process- och kvalitetsparametrar, där kvalitetsparametrarna beskriver produktkvalitén. Det är av intresse att studera hur processparametrarna påverkar produktkvalitén. Processparametrarna kan mätas vid vilket tidpunkt som helst genom olika sensorer. Produktkvalitén kan bestämmas först efter att järnmalmspelletsen är färdigreducerad. Därför präglas processen av en tidsfördröjning mellan mätningen av processparametrarna och labanalysemätningarna av kvalitetsparametrarna. På grund av tidsfördröjningen är det av intresse att kunna prediktera produktkvalitén utifrån processparametrarna. Om det går att prediktera produktkvalitén, är det av vikt att kunna avgöra prediktionens giltighet.  Examensarbetets syfte är att identifiera hur reduktionsprocessparametrarna påverkar reducerade järnets kvalitetsparametrar. En processövervakningsmetod som passar för processövervakning ska testas och undersökas utifrån hur metoden kan användas för att avgöra prediktionens giltighet. Processövervakningen ska användas för att avgöra om processen befinner sig i ett processläge som bidrar till en någorlunda korrekt och lämplig prediktion av produktkvalitén.  För analys av data användes 65 processparametrar och 6 kvalitetsparametrar. Den multivariata analysmetoden Partial Least Squares (PLS) användes för att nå syftet med examensarbetet. Via PLS skapades en modell som kunde beskriva vilka processparametrar som påverkade kvalitetsparametrarna samt hur processparametrarna påverkade kvalitetsparametrarna. PLS-modellen kunde prediktera kvalitetsparametrarna någorlunda korrekt och lämpligt, givet att processen befinner sig inom ramen för modellen och att det är en hög förklaringsgrad för kvalitetsparametern som predikteras. Kvalitetsparametern Y6-1 predikterades sämre eftersom förklaringsgraden för Y6-1 var låg. Processövervakningsmetoden som testades och undersöktes var PLS-övervakning. För att undersöka hur PLS-övervakning kan användas för att avgöra prediktions giltighet, användes tre processövervakningsverktyg. Dessa var X-scores processövervakning, Hotelling T2 och SPE. Resultatet var att PLS-övervakning kunde angiva hur processen förhåller sig till modellen. Observationerna som avvek i PLS-övervakningen predikterades sämre. Därmed kunde information om prediktionens giltighet genom PLS-övervakning erhållas. Att tillämpa PLS-övervakning för att avgöra prediktionens giltighet är en större framgång. Detta på grund av att information om produktkvalitén innan reduktionsprocessen är genomförd kan användas för att säkerställa produktion med tillfredställande kvalitet. Att tillämpa multivariata processövervakningsmetoder för att övervaka de predikterade kvalitetsparametrarna kan vara av intresse för framtida studier. Detta då processövervakningen kan användas för att minimera den interna variationen hos kvalitetsparametrarna. / Hybrit development AB strives to produce fossil-free steel by using hydrogen for the direct reduction process of iron ore pellets. To achieve that goal, Hybrit has carried out experimental campaigns where the feasibility of direct reduction using hydrogen gas has been investigated and studied. The quality of the reduced iron must be considered when producing iron and steel. The reduction process is characterized by a variety of process- and quality parameters. Because the quality parameters describe the quality of the product, it is of interest to study how the process parameters affect the quality parameters. The process parameters can be measured at any time through various sensors around the reactor in which the iron ore pellets are reduced. While the quality of the product can only be determined after the iron ore pellets have been completely reduced. Therefore, the process is characterized by a time delay between the measurement of the process parameters and the measurement of the quality parameters, where the reduced iron must be analyzed in a laboratory before the quality parameters can be measured. Because of the time delay, it is of interest to be able to predict the quality of the product based on the process parameters. If it is possible to predict the quality, then it is of importance to be able to determine the validity of the prediction.  The aim of this master thesis is to identify how the reduction process parameters affect the quality parameters of the reduced iron. A process monitoring method suitable for monitoring the process need be tested and investigated based on how the method can be used to determine the validity of the prediction. The process monitoring will be used to determine whether the process is in a process state that contributes to a reasonably accurate and appropriate prediction of the quality of the product.  65 process parameters and 6 quality parameters were used for the analysis of how the reduction process parameters affect the quality parameters of the reduced iron. The multivariate analysis method Partial Least Squares (PLS) was used to achieve the aim of the thesis. A multivariate model which could describe how the process parameters affect the quality parameters was created through PLS. The PLS-model was able to predict the quality parameters reasonably correctly and appropriately, given that the process is within the scope of the model and that the explanatory power is high for the quality parameter that is predicted. The quality parameter Y6-1 could not be predicted reasonably correct as the explanatory power for Y6-1 was low. The process monitoring method tested and investigated was PLS monitoring. Three process monitoring tools were used when PLS monitoring was investigated based on how they can be used to determine the validity of the prediction. These tools were X-scores process monitoring, Hotelling T2 and SPE. The result was that PLS monitoring could indicate how the process relates to the model. Observations that deviated in the PLS monitoring could not be predicted correctly. Thus, information about the validity of the prediction through PLS monitoring could be obtained. Applying PLS monitoring to determine the validity of the prediction is a greater success. This is because information about the quality of the product before the reduction process is completed can be used to ensure production with a satisfactory product quality. Applying multivariate process monitoring methods to monitor the predicted quality parameters may be of interest for future studies. This is because the process monitoring can be used to minimize the internal variation of the quality parameters.

Polymer powder bed fusion (PBF/P) is one of many additive manufacturing (AM) processes utilized for producing polymer parts from digital 3D models. AM is preferred over traditional manufacturing methods in many applications due to advantages including tool-less manufacturing, high geometric complexity, short lead times, and reduced material waste. However, many industries that stand to benefit the most from AM are limited in their ability to use AM parts in large part due to low confidence in AM part quality. Among the polymer AM processes, PBF/P processes show significant promise for these applications due to their comparatively high isotropy and mechanical properties. Due to the many process variables present in PBF/P, printing conditions can vary from print to print resulting in poor repeatability of physical properties in printed parts. Many approaches have been studied for addressing this issue such as modeling of print dynamics, print parameter optimization, and process control. However, PBF/P remains largely unutilized in applications where quality control and assurance are high priorities. This work presents a novel approach for in-situ process monitoring and control in PBF/P and is demonstrated for the large area projection sintering (LAPS) process. The method proposed in this study monitors the powder bed surface via visible light cameras and identifies critical steps in the melting process defined as optical melting states (OMSs). The relationship between print parameters, process signatures, and resulting physical properties are studied. This thesis shows that during melting, the changing surface geometry and optical properties of the powder bed can be effectively monitored with optical cameras and are strongly correlated with the final density and ultimate tensile strength (UTS) of the printed part. By implementing closed-loop OMS control, consistent physical properties can be obtained despite different processing conditions. While established methods of identifying the property plateau for other PBF/P processes are not effective for the LAPS process, such as energy density methods, OMS control has been shown to effectively achieve full density and UTS in LAPS parts while optimizing print time. However, OMS methods are limited in their ability to evaluate ductility and percent crystallinity.

Laser powder bed fusion (LPBF) remains a predominately open-loop additive manufacturing process with minimal in-situ quality and process control. Some machines feature optical monitoring systems but lack automated analytical capabilities for real-time defect detection. Recent advances in machine learning (ML) and convolutional neural networks (CNN) present compelling solutions to analyze images in real-time and to develop in-situ monitoring. Approximately 30,000 selective laser melting (SLM) build images from 31 previous builds are gathered and labeled as either “okay” or “defect”. Then, 14 open-sourced CNN were trained using transfer learning to classify the SLM build images. These models were evaluated by F1 score and down selected to the top 3 models. The top 3 models were then retrained and evaluated using Dietterich’s 5x2 cross-validation and compared with pairwise student t-tests. The pairwise t-test results show no statistically significant difference in performance between VGG- 19, Xception, and InceptionResNet. All models are strong candidates for future development and refinement. Additional work addresses the entire model development process and establishes a foundation for future work. Collaborations with computer science students has produced an image pre-processing program to enhance as-taken SLM images. Other outcomes include initial work to overlay CAD layer images and preliminary hardware integration plan for the SLM machine. The results from this work have demonstrated the potential of an optical layer-wise image defect detection system when paired with a CNN.

Yes / Industry-standard quality management systems such as Six Sigma and emerging Industry 4.0 compliant production processes demonstrate the importance of in-line condition monitoring of manufacturing methods for achieving the highest levels of product quality. Measurement data collected as the process is running can inform the operator about unexpected changes in machine operation or raw materials that could negatively impact production; and offer an opportunity for a process control intervention to stabilise production. However, micro-manufacturing production lines can pose a challenging environment for deploying such systems, since processing events can occur extremely rapidly and in harsh environments. Moreover, the small scale of micro-nano featured components can make sensor installation even more problematic. Recently, ultrasonic micromoulding has drawn attention in niche markets due to its unique advantages for processing thermoplastics as a new micro-manufacturing technology. The process differs from conventional moulding significantly by eliminating the need for a plasticising screw and using direct application of ultrasonic energy to melt the polymer. This offers numerous benefits such as decrease in energy usage, moulding at lower pressures, easier cleaning, and reduced material residence times, the latter which could be beneficial for pharma-grade polymers or polymers with active ingredients. However, very little work has been reported attempting to monitor the process using in-line measurements. This work aims to evaluate the characteristics of the ultrasonic micromoulding process for microinjection moulding of a microneedle array using a range of sensor technologies including: data recorded by the machine controller; a high-speed thermal camera and a cavity pressure transducer. The data has captured the highly dynamic process environment with a high degree of accuracy. The relationship between the process data and dimensional quality of the ultrasonically micromoulded products has been quantified and subsequently implemented as a cost-effective in-line quality assurance method. / Horizon 2020, the EU Framework Programme for Research and Innovation (Project ID: 674801). This research has also received funding and support from two other Horizon 2020 projects: HIMALAIA (Grant agreement No. 766871) and Laser4Fun (GA no. 675063)