Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry

Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as transport (including the aeronautical, automotive and railway industries), and energy or biomedical industries. As in the case of many other manufacturing technologies, grinding relies heavily on the experience and knowledge of the operatives. For this reason, considerable efforts have been devoted to generating a systematic and sustainable approach that reduces and eventually eliminates costly trial-and-error strategies. The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The structure of the DT comprises four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for process models; and (4) knowledge integration in a configurable open-source industrial tool. To this end, intensive collaboration between all the involved agents (from university to industry) is essential. One of the most remarkable results is the development of new and more realistic models for predicting wheel wear, which currently can only be known in industry through costly trial-and-error strategies. Also, current work is focused on the development of an intelligent grinding wheel, which will provide on-line information about process variables such as temperature and forces. This is a critical issue in the advance towards a zero-defect grinding process.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project “Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low-Pressure Turbines” (DPI2017-82239-P)

Height to Crown Base Modelling for the Main Tree Species in an Even-Aged Pedunculate Oak Forest: A Case Study from Central Croatia

The height to crown base (hcb) is a critical measure used in many investigations as an input variable to investigate the vigour of the stands, the social position of the trees, and to evaluate the behaviour of forest fires, among other uses. Though measuring height-related variables in the field is always time-consuming, the foremost benefits offered by modelling hcb are that it permits to generalize and average a very uneven attribute and, furthermore, provides insights about which tree and stand variables have a significant impact on hcb. However, there are many species in which models of the crown base height have not been developed in Croatia. The objective of this research was to develop a height to base crown model for each of the main species present in the two-layered mixed stands of this study. According to previous investigations, logistic models provide the highest precision and require the lowest inventory cost owing to less frequent measurements. Tree- and plot-level variables with distance-independent competition indexes were studied in the fitting model. In this research, we obtained models for the main stand species: Acer campestre (root mean squared error (RMSE) = 2.28 m, R2 = 82.80%); Alnus glutinosa (RMSE = 1.78 m, R2 = 85.36%); Carpinus betulus (RMSE = 2.47 m, R2 = 67.55%); Fraxinus angustifolia (RMSE = 2.46 m, R2 = 82.45%); Quercus robur (RMSE = 2.60 m, R2 = 80.57%); Tilia sp. (RMSE = 2.01 m, R2 = 89.07%); and Ulmus laevis (RMSE = 1.71 m, R2 = 92.42%). The combination of the total height, tree, and plot-level variables with distance-independent competition indexes contributed to the prediction accuracy of proposed model significantly

Potencial energético de los bosques y costes de suministro para Soria, España

Soria is a forested province in Northern Spain. The utilization level of the forests in Soria is low at present, but it is predicted to rise in the future. Because of the high altitude, heating is also needed. These form a good basis for increasing the use of wood chips in energy production. In this study, a procedure to estimate the potential of wood chip from forests and their procurement costs was adapted to Spanish conditions. The harvesting potential was estimated to be between 140,000 m3 and 280,000 m3 in 2010, and to double by 2030. Cost-supply curves were provided to aid in the planning of heating plant investments. Compared to European cost levels, the procurement costs in Soria are not high.Soria es una provincia con gran tradición forestal situada en la mitad norte de España. Actualmente el nivel de cortas en los bosques de Soria es bajo aunque se prevé que aumente en el futuro. Al ser una provincia montañosa, de clima frío en invierno, el uso de calefacción es necesario durante bastantes meses al año. Esto hace que sea interesante incrementar el uso de astillas de madera para producir energía. En este estudio, se adaptó a las condiciones españolas un procedimiento para estimar fuentes de biomasa leñosa y sus costes de suministro. Se ha estimado un potencial de aprovechamientos para 2010 que oscila entre los 140.000 y 280.000 m3, y el doble para 2030. Se han utilizado curvas de coste de suministro para ayudar a planificar las inversiones en plantas de producción de calor. Los costes de suministro en Soria no son altos comparados con el nivel de costes europeo

eCOMMONtech: plataforma sofrware para monitorización del balance de Gases de Efecto Invernadero en el Marco de Mecanismos de Desarrollo Limpio Forestales y Proyectos REDD+

La monitorización de las condiciones que debe cumplir un área forestal en proyectos MDL o REDD de manera tradicional, es decir, mediante mediciones y controles in situ, conlleva unos costes difíciles de asumir. Por ello, se ha planteado el desarrollo de una metodología capaz de integrar tecnologías orientadas a la realización de inventarios de carbono en áreas forestales de países en vías de desarrollo, mediante la utilización de diferentes tecnologías (sensorización ambiental, teledetección espacial, técnicas forestales, internet, etc.) que permiten determinar aquellos procedimientos más eficaces desde el punto de vista de la calidad y fiabilidad de la información obtenida y del coste/beneficio; analizando, las mejoras que suponen frente a los métodos tradicionales. Para ello, se desarrollan algoritmos y métodos de análisis necesarios para extraer las variables e indicadores medioambientales con el fin de realizar la monitorización de los ciclos de carbono en ámbitos forestales atribuibles a proyectos de absorciones de CO2.El resultado es la creación de una plataforma web que permite la monitorización remota y en tiempo real de inventarios de carbono a través de la integración de datos provenientes de sistemas de sensorización, imágenes tratadas con tecnologías de observación de la tierra y datos de campo

Comparison of machine learning algorithms for wildland-urban interface fuelbreak planning integrating ALS and UAV-Borne LiDAR data and multispectral images

Producción CientíficaControlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.Ministerio de Economía, Industria y Competitividad (DI-16-08446; DI-17-09626; PTQ-16-08411; PTQ- 16-08633)European Commission through the project ‘MySustainableForest’ (H2020-EO-2017; 776045

Comparison of stem taper equations for eight major tree species in the Spanish Plateau

<p class="Articulo"><em>Aim of study:</em><strong> </strong>A stem taper function and a compatible merchantable volume system are compared to evaluate which provides a better description of the stem profile for the main species in central Spain.</p><p class="Articulo"><em>Area of study:</em><strong> </strong>This research was carried out in the region of Castile-Leon, located in Central Spain.<strong></strong></p><p class="Articulo"><em>Material and Methods:</em> A total of 6,357 trees were selected for destructive sampling. All models were fitted using a first-order continuous autoregressive error structure to address the problem of autocorrelation.<strong></strong></p><p class="Articulo"><em>Main results:</em> In terms of accuracy, the root mean square error (RMSE) in both models ranged from 0.75 to 2.72 depending on the species analyzed, presenting values similar to those reported in other studies. Small differences in the goodness-of-fit for both procedures were also found, and the Stud model provided better accuracy for 6 of the 8 species studied, with RMSE reductions of 0.5% to 8.6%. The RMSE obtained in the cross-validation phase was on average 1.22 times higher than what was obtained in the fitting phase.<strong></strong></p><p class="Articulo"><em>Research highlights:</em><strong> </strong>The non-linear extra sum of squares method indicated that the stem taper differs among the five softwood species and three hardwood species. In hardwoods, the first inflection point is lower than in softwoods (at around 5%) and the second inflection point is higher (at around 85%) than those of softwoods.</p><p class="Articulo"><strong>Keywords</strong>: taper function; volume system; Central Spain; softwoods; hardwoods.</p

Desarrollo de ecuaciones de copa para "Pinus pinaster" Ait. en el Sistema Ibérico meridional

Las ecuaciones predictivas de los parámetros de copas son habitualmente utilizadas como relaciones auxiliares para la construcción de modelos de crecimiento. En el presente trabajo, se han desarrollado ecuaciones de copa para Pinus pinaster Ait., que servirán para un proceso de retrotraer los datos (backdating) para la creación de un modelo de crecimiento diamétrico. Además, se utiliza la predicción de los parámetros de copa para demostrar la escasa influencia de la zona de la copa comprendida entre la altura a la base de la copa y la altura donde la copa adquiere su máxima anchura

Non-destructive measurement techniques for taper equation development: a study case in the Spanish Northern Iberian Range

In recent years, the technology for measuring the diameter and height of standing trees has improved significantly. These enhancements allow estimation of the volume of standing trees using stem taper equations, which traditionally have been constructed with data from felled trees, in an accurate and economically feasible way. A nondestructive method was evaluated with data from 38 pines and was validated with data from another 38 pines, both in the Northern Iberian Range (Spain). The electronic dendrometer Criterion RD1000 (Laser Technology Inc.) and the laser hypsometer TruPulse (Laser Technology Inc.) were used due to their accuracy and interoperability. The methodology was valid (unbiased and precise) measuring from a distance similar to the height of the tree. In this distance, statistical criteria and plots based on the residuals showed no clear advantage in volume estimation with models fitted with data from destructive methods against models fitted with data from the proposed non-destructive technique. This methodology can be considered useful for individual volume estimation and for developing taper equations

Individual-tree growth system for even-aged Aleppo pine plantations in Aragón, Spain

Aim of study: An individual-tree growth system was developed for Aleppo pine (Pinus halepensis Mill.) plantations. Area of study: Aragón region (Northeast Spain). Material and methods: Two datasets were used: Second and Third Spanish National Forest Inventories (104 plots with 1,678 trees), and ad hoc permanent plots (58 plots with 1720 trees, including 36 dead trees). Individual tree growth system was based on nine models. Different combinations of yield classes, initial stocking rates, thinning parameters, rotation periods, and age at first thinning were evaluated through the three most representative scenarios: timber production; soil conservation and biodiversity enhancement. Main results: The nine models demonstrated a significant explanatory power for the data, with R2 values ranging from 0.71 to 0.99. These findings are consistent with previous research, indicating a strong goodness of fit. Additionally, yield tables were developed for the three prevalent silvicultural scenarios. To enhance usability, all models within the system were seamlessly integrated into a web-based application SIMANFOR. Research highlights: To date, Aleppo pine forest managers in Aragon could only simulate silvicultural scenarios in natural stands. This study provides a new tool for plantations