State estimators in soft sensing and sensor fusion for sustainable manufacturing

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables which cannot be measured directly (’soft sensing’) or for which only noisy, intermittent, delayed, indirect or unreliable measurements are available, perhaps from multiple sources (’sensor fusion’). In this paper we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes. It is shown that state estimation algorithms can play a key role in manufacturing systems to accurately monitor and control processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and control of distributed manufacturing systems

High speed milling technological regimes, process condition and technological equipment condition influence on surface quality parameters of difficult to cut materials

[ES] La calidad superficial en las piezas mecanizadas depende del acabado superficial, resultado de las marcas dejadas por la herramienta durante el proceso de corte. Las aproximaciones teóricas tradicionales indican que estas marcas están relacionadas con los parámetros de corte (velocidad de corte, avance, profundidad de corte...), el tipo de máquina, el material de la pieza, la geometría de la herramienta, etc. Pero no todos los tipos de mecanizado y selección de materiales pueden dar un resultado ambiguo. Hoy en día, de manera progresiva, se están utilizando las técnicas de fresado de Alta Velocidad sobre materiales de difícil mecanizado cada vez más. El fresado de Alta Velocidad implica a un considerable número de parámetros del proceso que pueden afectar a la formación topográfica 3D de la superficie. La hipótesis de que los parámetros de rugosidad superficial dependen de las huellas dejadas por la herramienta, determinadas por las condiciones de trabajo y las propiedades del entorno, condujo al desarrollo de una metodología de investigación personalizada. Este trabajo de investigación muestra como la combinación de los parámetros, inclinación del eje de la herramienta, deflexión geométrica de la herramienta y comportamiento vibracional del entorno, influencian sobre el parámetro de rugosidad superficial 3D, Sz. El modelo general fue dividido en varias partes, donde se ha descrito la influencia de parámetros del proceso adicionales, siendo incluidos en el modelo general propuesto. El proceso incremental seguido permite al autor desarrollar un modelo matemático general, paso a paso, testeando y añadiendo los componentes que más afectan a la formación de la topografía de la superficie. En la primera parte de la investigación se seleccionó un proceso de fresado con herramientas de punta plana. Primero, se analiza la geometría de la herramienta, combinada con múltiples avances, para distinguir los principales parámetros que afectan a la rugosidad superficial. Se introduce un modelo de predicción con un componente básico para la altura de la rugosidad, obtenida por la geometría de la herramienta de corte. A continuación, se llevan a cabo experimentos más específicamente diseñados, variando parámetros tecnológicos. Esto empieza con el análisis de la inclinación del eje de la herramienta contra la mesa de fresado. Los especímenes de análisis son muestras con cuatro recorridos de corte rectos con corte en sentido contrario. Las trayectorias lineales con diferentes direcciones dan la oportunidad de analizar la inclinación del husillo de fresado en la máquina. Un análisis visual reveló diferencias entre direcciones de corte opuestas, así como marcas dejadas por el filo posterior de la herramienta. Considerando las desviaciones de las marcas de corte observadas en las imágenes de rugosidad superficial obtenidas a partir de las medidas, se introdujo un análisis sobre el comportamiento dinámico del equipo y de la herramienta de corte. Las vibraciones producen desviaciones en la mesa de fresado y en la herramienta de corte. Estas desviaciones fueron detectadas e incluidas en el modelo matemático para completar la precisión en la predicción del modelo. Finalmente, el modelo de predicción del parámetro de rugosidad Sz fue comprobado con un mayor número de parámetros del proceso. Los valores de Sz medidos y predichos, fueron comparados y analizados estadísticamente. Los resultados revelaron una mayor desviación de la rugosidad predicha en las muestras fabricadas con diferentes máquinas y con diferentes avances. Importantes conclusiones sobre la precisión del equipo de fabricación han sido extraídas y de ellas se desprende que la huella de la herramienta de corte está directamente relacionada con los parámetros de la topografía de la superficie. Además, la influencia de la huella está afectada por la geometría de la herramienta de corte, la rigidez de la herramienta y la precisión del equipo. La geometría de la herramienta conforma la base del parámetro Sz, desviación de la altura de la superficie. Las conclusiones alcanzadas son la base para recomendaciones prácticas, aplicables en la industria.[CA] La qualitat superficial en les peces mecanitzades depèn de l'acabat superficial, resultat de les marques deixades per l'eina durant el procés de tall. Les aproximacions teòriques tradicionals indiquen que aquestes marques estan relacionades amb els paràmetres de tall (velocitat de tall, avanç, profunditat de tall...), el tipus de màquina, el material de la peça, la geometria de l'eina, etc. Però no tots els tipus de mecanitzat i selecció de materials poden donar un resultat ambigu. Avui en dia, de manera progressiva, s'estan utilitzant les tècniques de fresat d'Alta Velocitat sobre materials de difícil mecanització cada vegada més. El fresat d'Alta Velocitat implica un considerable nombre de paràmetres del procés que poden afectar la formació topogràfica 3D de la superfície. La hipòtesi que els paràmetres de rugositat superficial depenen de les empremtes deixades per l'eina, determinades per les condicions de treball i les propietats de l'entorn, va conduir al desenvolupament d'una metodologia d'investigació personalitzada. Aquest treball de recerca mostra com la combinació dels paràmetres, inclinació de l'eix de l'eina, deflexió geomètrica de l'eina i comportament vibracional de l'entorn, influencien sobre el paràmetre de rugositat superficial 3D, Sz. El model general va ser dividit en diverses parts, on s'ha descrit la influència de paràmetres addicionals del procés, sent inclosos en el model general proposat. El procés incremental seguit permet a l'autor desenvolupar un model matemàtic general, pas a pas, testejant i afegint els components que més afecten a la formació de la topografia de la superfície. En la primera part de la investigació es va seleccionar un procés de fresat amb eines de punta plana. Primer, s'analitza la geometria de l'eina, combinada amb múltiples avanços, per distingir els principals paràmetres que afecten la rugositat superficial. S'introdueix un model de predicció amb un component bàsic per a l'altura de la rugositat, obtinguda a través de la geometria de l'eina de tall. A continuació, es duen a terme experiments més específicament dissenyats, variant paràmetres tecnològics. Això comença amb l'anàlisi de la inclinació de l'eix de l'eina contra la taula de fresat. Els espècimens d'anàlisi són mostres amb quatre recorreguts de tall rectes amb tall en sentit contrari. Les trajectòries lineals amb diferents direccions donen l'oportunitat d'analitzar la inclinació del fus de fresat en la màquina. Una anàlisi visual revelà diferències entre direccions de tall oposades, així com marques deixades pel tall posterior de l'eina. Considerant les desviacions de les marques de tall observades en les imatges de rugositat superficial obtingudes a partir de les mesures, es va introduir una anàlisi sobre el comportament dinàmic de l'equip i de l'eina de tall. Les vibracions produeixen desviacions en la taula de fresat i en l'eina de tall. Aquestes desviacions van ser detectades i incloses en el model matemàtic per completar la precisió en la predicció de el model. Finalment, el model de predicció de el paràmetre de rugositat Sz va ser comprovat amb un major nombre de paràmetres del procés. Els valors de Sz mesurats i predits, van ser comparats i analitzats estadísticament. Els resultats van revelar una major desviació de la rugositat predita en les mostres fabricades amb diferents màquines i amb diferents avanços. Importants conclusions sobre la precisió de l'equip de fabricació han estat extretes i d'elles es desprèn que l'empremta de l'eina de tall està directament relacionada amb els paràmetres de la topografia de la superfície. A més, la influència de la empremta està afectada per la geometria de l'eina de tall, la rigidesa de l'eina i la precisió de l'equip. La geometria de l'eina conforma la base del paràmetre Sz, desviació de l'altura de la superfície. Les conclusions assolides són la base per recomanacions pràctiques, aplicables en la indústria.[EN] Surface quality of machined parts highly depends on the surface texture that reflects the marks, left by the tool during the cutting process. The traditional theoretical approaches indicate that these marks are related to the cutting parameters (cutting speed, feed, depths of cut...), the machining type, the part material, the tool geometry, etc. But, different machining type and material selection can give a variable result. In nowadays, more progressively, High Speed milling techniques have been applied on hard-to-cut materials more and more extensively. High-speed milling involves a considerable number of process parameters that may affect the 3D surface topography formation. The hypothesis that surface topography parameters depends on the traces left by the tool, determined by working conditions and environmental properties, led to the development of a custom research methodology. This research work shows how the parameters combination, tool axis inclination, tool geometric deflection, cutting tool geometry and environment vibrational behavior, influence on 3D surface topography parameter Sz. The general model was divided in multiple parts, where additional process parameters influence has been described and included in general model proposed. The incremental process followed allows the author to develop a general mathematical model, step by step, testing and adding the components that affect surface topography formation the most. In the first part of the research a milling procedure with flat end milling tools was selected. First, tool geometry, combined with multiple cutting feed rates, is analyzed to distinguish the main parameters that affect surface topography. A prediction model is introduced with a basic topography height component, performed by cutting tool geometry. Next, specifically designed experiments were conducted, varying technological parameters. That starts with cutting tool axis inclination against the milling table analysis. The specimens of analysis are samples with 4 contrary aimed straight cutting paths. Linear paths in different directions give a chance to analyze milling machine spindle axis topography, as well as marks left from cutting tool back cutting edge. Considering the deviations of cutting marks observed in the images of the surface topography obtained through the measurements, the milling equipment and cutting tool dynamical behavior analysis were introduced. Vibrations produce deviations in the milling table and cutting tool. These deviations were detected and included in the mathematical model to complete the prediction model accuracy. Finally, the prediction model of the topography parameter SZ was tested with increased number of process parameters. Measured and predicted SZ values were compared and analyzed statistically. Results revealed high predicted topography deviation on samples manufactured with different machines and with different feed rates. Relevant conclusions about the manufacturing equipment accuracy have been drawn and they state that cutting tool's footprint is directly related with surface topography parameters. Besides, footprint influence is affected by cutting tool geometry, tool stiffness and equipment accuracy.Logins, A. (2021). High speed milling technological regimes, process condition and technological equipment condition influence on surface quality parameters of difficult to cut materials [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/164122TESI

Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.ThisinvestigationwaspartiallysupportedbyProjects TIN2011-24046,IPT-2011-1265-020000andDPI2009- 06124-E/DPIoftheSpanishMinistryofEconomyand Competitiveness.WethanktheUFIinMechanicalEngineer- ing oftheUPV/EHU(UFIMECA-1.0.2016(ext))forits support

Model-based Tool Condition Monitoring for Ball-nose End Milling

Ph.DDOCTOR OF PHILOSOPH

Prediction of machining accuracy based on geometric error estimation of tool rotation profile in five-axis multi-layer flank milling process

In five-axis multi-layer flank milling process, the geometric error of tool rotation profile caused by radial dimension error and setup error has great influence on the machining accuracy. In this work, a new comprehensive error prediction model considering the inter-layer interference caused by tool rotation profile error is established, which incorporates a pre-existing prediction model dealing with a variety of errors such as geometric errors of machine tool, workpiece locating errors, and spindle thermal deflection errors. First, a series of tool contact points on the tool swept surface in each single layer without overlapping with others are calculated. Second, the position of the tool contact points on the overlapped layers is updated based on the detection and calculation of inter-layer interferences. Third, all evaluated tool contact points on the final machined surface are available for completing the accuracy prediction of the machined surface. A machining experiment has been carried out to validate this prediction model and the results show the model is effective

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Data-Efficient Estimation of Remaining Useful Life for Machinery with a Limited Number of Run-to-Failure Training Sequences

Prognostics and Health Monitoring (PHM) of machinery is a research area with great relevance to industrial applications as it can serve as a foundation for safer, more cost-efficient operation and maintenance. The prediction of Remaining Useful Life (RUL) plays an important part in this field and has seen significant advances from the introduction of machine learning methods. However, these methods typically require model training with a large number of run-to-failure sequences, which are often not feasible to obtain due to the required time and cost investments. The present study addresses this issue by introducing a novel methodology, which first quantifies the deviation from the machine’s health and fault state and then calculates a machine Health Index (HI) prior to the prediction of RUL. In addition, the start of a degradation state is determined. Alternative implementations of the proposed methodology are compared utilising several methods, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network (NN), Mahalanobis Distance (MD), and LSTM Autoencoder (AE) NN. The methodology is applied to the open turbofan degradation (C-MAPSS) and bearing vibration (FEMTO-ST PROGNOSTIA) datasets. When a reduced subset of training sequences is used, the prediction results demonstrate that the proposed methodology largely outperforms the baseline method without HI generation. For example, when comparing prediction errors of the C-MAPSS dataset at a reduction of the available number of training sequences to 5%, the proposed method shows an average prediction improvement by 6.5% - 19.2% relative to the baseline method. The presented approach is therefore suitable to improve model generalisation for cases with a limited number of training sequences. When the full training set is utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method. Hence, an additional contribution of the presented data-efficient approach is the reduction of required computing resources, which has implications on training time, energy consumption, and environmental impact.10.13039/501100000266-Engineering and Physical Sciences Research Council (EPSRC

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture