Desarrollo de un algoritmo de control de una estación depuradora de aguas residuales

Treball final de Màster Universitari en Enginyeria Industrial. Codi: SJA020. Curs acadèmic: 2017/2018El objetivo de este proyecto es el desarrollo de un algoritmo de control de una estación depuradora de aguas residuales (EDAR) que permita un mayor ahorro energético y económico que los actuales métodos de control de estas estaciones. Para ello, se van a utilizar técnicas de predicción de señales, así como métodos de control predictivo

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Variation propagation of bench vises in multi-stage machining processes

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)Variation propagation has been successfully modeled by the Stream of Variation (SoV) approach in multistage machining processes. However, the SoV model basically supports 3-2-1 fixtures based on punctual locators and other workholding systems such as conventional vises are not considered yet. In this paper, the SoV model is expanded to include the fixture- and datum-induced variations on workholding devices such as bench vises. The model derivation is validated through assembly and machining simulations on Computer Aided Design software. The case study analyzed shows an average error of part quality prediction between the SoV model and the CAD simulations of 0.26%

Economic model predictive control of wastewater treatment plants based on BSM1 using linear prediction models

Comunicación presentada en IEEE 15th International Conference on Control and Automation (ICCA), 16-19 July 2019, Edinburgh, United Kingdom,.In this paper, we have developed an Economical Model Predictive Control (EMPC) for a Wastewater Treatment Plant (WWTP) with the use of a standard semidefinite programming solver. In this case, the objective has been to keep the ammonium concentration in the effluent under limits manipulating the air insufflation pumps at the biological reactor and an internal recycle valve. The minimized cost function consists of the product of the energy consumed by the air insufflator and the cost of the electricity, taking into account the variations of the tariffs over the day. We have simulated the behaviour of the WWTP using the Benchmark Model Simulation n° 1 (BSMI), and we have developed a linear prediction model in order to apply the EMPC method

A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes

In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that express the propagation of manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear physical-based models such as the Stream of Variation (SoV) model are commonly used, but its accuracy may be limited when applied to MMPs with a large amount of stages, mainly because of the modeling errors at each stage that are accumulated downstream. In this paper we propose a methodology to calibrate the SoV model using data from the inspection stations and prior engineering-based knowledge. The data used for calibration does not contain information about the sources of variation, and they must be estimated as part of the model adjustment procedure. The proposed methodology consists of a recursive algorithm that minimizes the difference between the sample covariance of the measured Key Product Characteristic (KPC) deviations and its estimation, which is a function of a variation propagation matrix and the covariance of the deviation of the variation sources. To solve the problem with standard convex optimization tools, Schur complements and Taylor series linearizations are applied. The output of the algorithm is an adjusted model, which consists of a variation propagation matrix and an estimation of the aforementioned variation source covariance. In order to validate the performance of the algorithm, a simulated case study is analyzed. The results, based on Monte Carlo simulations, show that the estimation errors of the KPC deviation covariances are proportional to the measurement noise variance and inversely proportional to the number of processed parts that have been used to train the algorithm, similarly to other process estimators in the literature.Funding for open access charge: CRUE-Universitat Jaume

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

In machining processes, underusing and overusing cutting tools directly affect part quality, entailing economic and environmental impacts. In this paper, we propose and compare different strategies for tool replacement before processed parts exceed surface roughness specifications without underusing the tool. The proposed strategies are based on an online part quality monitoring system and apply a model-based algorithm that updates their parameters using adaptive recursive least squares (ARLS) over polynomial models whose generalization capabilities have been validated after generating a dataset using theoretical models from the bibliography. These strategies assume that there is a continuous measurement of power consumption and a periodic measurement of surface roughness from the quality department (scarce measurements). The proposed strategies are compared with other straightforward tool replacement strategies in terms of required previous experimentation, algorithm simplicity and self-adaptability to disturbances (such as changes in machining conditions). Furthermore, the cost of each strategy is analyzed for a given benchmark and with a given batch size in terms of needed tools, consumed energy and parts out of specifications (i.e., rejected). Among the analyzed strategies, the proposed model-based algorithm that detects in real-time the optimal instant for tool change presents the best results.Funding for open access charge: CRUE-Universitat Jaume

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Nowadays, advanced manufacturing models, such as the stream-of-variation (SoV) model, have been successfully applied to derive the complex relationships between fixturing, manufacturing, and datum errors throughout a multistage machining process. However, the current development of the SoV model is still based on 3-2-1 fixturing schemes, and although some improvements have been done, e.g., N-2-1 fixtures, the effect of general workholding systems, such as bench vices or three-jaw chucks, has not yet been included into the model. This article presents the extension of the SoV model to include fixture and datum errors considering both bench vices and three-jaw chucks as fixturing devices in multistage machining processes. The model includes different workholding configurations, and it is shown how to include the workholding accuracy to estimate part quality. The extended SoV model is validated in a three-stage machining process by both machining experimentation and CAD simulations

A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes

Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data

Modeling, fault diagnosis and prognosis under the Zero Defect Manufacturing paradigm

This thesis addresses several problems that may arise during the development of Zero Defect Manufacturing strategies. First, we have proposed variation propagation models for vices and self-centering three-jaw chucks using the Stream-of-Variation methodology. We have developed a methodology to adjust linear input-output models using physical-based models, process data and engineering knowledge. We have also proposed a procedure to detect and isolate faults online in a process with the objective to reduce the number of required measurements, using models from the process plan. We have also addressed the problem of predicting the remaining useful life of cutting tools. First, we have proposed a methodology to estimate the surface roughness of the processed parts when those measurements are unavailable, using indirect measurements and a steady-state Kalman filter observer. Additionally, we have proposed a prognosis methodology to predict the optimal moment to replace the cutting tool using an adaptive recursive least squares algorithm.Programa de Doctorat en Tecnologies Industrials i Material

Desarrollo de una plataforma para la prueba de algoritmos de control sobre redes inalámbricas impementables mediante estándares industriales

Treball Final de Grau en Enginyeria en Tecnologies Industrials. Codi: ET1040. Curs: 2015/2016El objetivo de este proyecto consiste en el diseño y desarrollo de una plataforma que permita la prueba de algoritmos de control aplicados sobre redes industriales, tanto cableadas como inalámbricas, así como la prueba de algoritmos para el control de procesos. Se pretende que el usuario de dicha plataforma pueda posteriormente almacenar los datos obtenidos durante los experimentos para después analizarlos y estudiarlos mediante el uso de otros programas de cálculo.El ámbito de aplicación del presente proyecto se encuentra en el campo de la investigación del control continuo de sistemas a través de redes de comunicación. Este proyecto también puede ser aplicable al campo de la docencia, tanto para usos de demostración como para ser empleado en prácticas de laboratorio. De esta forma, durante una sesión de laboratorio diferentes grupos de trabajo podrían controlar a distancia, por turnos, un único dispositivo o modelo. Además, un desarrollo posterior de esta plataforma podría ser utilizado para la prueba de funcionamiento de dispositivos industriales comerciales