Integrated modeling with Top-Down approach in subsidiary industries

This article considers how conceptual design of industrial products is supported by current CAD systems. The case of subsidiary industries, or first tier suppliers, that must simultaneously deal with different customers and CAD platforms, receive special attention. Conceptual design is critical, since the large variety of fundamental product data managed (not just geometry) would be specified, modeled and interrelated (i.e. functional relations), to both simplify and ensure correctness and efficiency of the next design phases of current design, and make them easy to reuse, modify and redesign in the future. We give an approach to introduce conceptual design through top-down methodology and integrate it with final geometry. In this context, and in order to help subsidiary industries to improve their model quality, we propose the elaboration of product-oriented modeling guidelines, or “best modeling practices”, instead of CAD-oriented modeling guidelines. The approach has been validated by testing the conceptual design tools of two commercial high-end CAD systems at use in many subsidiary automotive industries

ParSketch: a Sketch-based Interface for a 2D Parametric Geometry Editor

ParSketch is a software prototype to evaluate the usability and functionality of a sketching interface aimed at defining 2D parametric sections. Currently, ParSketch interprets strokes which can be recognized as geometry (line, arc, circle, ellipse, or composed entities that are automatically segmented into those basic entities), or graphic gestures representing constraints (dimension, parallel, perpendicular, tangent, concentric, horizontal or vertical). From the functionality point of view, ParSketch compares to current commercial parametric CAD applications, as it offers many of the features provided by such applications. A theoretical analysis of the efficiency component of usability is provided that justifies the potential capability of sketching interfaces to compete with classical WIMP applications. Finally, a usability study is presented, which makes special emphasis in the satisfaction component of usability.The Spanish Ministry of Science and Education and the European Union (Project DPI2004-01373) supported this work. It was also partially supported by Fundació Caixa Castelló-Bancaixa under the Universitat Jaume I program for Research Promotion (Project P1-1B2004-02)

Special Issue: Image Analysis in Agriculture (Editorial)

Blasco, J.; Aleixos Borrás, MN.; Zude, M. (2014). Special Issue: Image Analysis in Agriculture (Editorial). Biosystems Engineering. 117:1-1. doi:10.1016/j.biosystemseng.2013.09.007S1111

Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study

[EN] In engineering, the 3D model of a product is essential. A 3D model allows making modifications by editing the characteristic modeling functions, which implies knowing in detail the process followed in the modeling. In addition, the flexibility in the configuration is limited, since the modifications are made on geometry and parameters that are dependent on each other. In this work, a parametric modeling approach allows the generation of 3D models with different specifications by modifying a reduced number of parameters. To demonstrate the functionality, an application has been developed, using Autodesk Inventor iLogic, for the modeling of an engine with V-cylinder arrangement. Taking as input key parameters (number of cylinders,¿), it can generate virtual prototypes with different configurations, facilitating the selection of the best product configuration by allowing to evaluate different alternatives.Veliz Vega, V.; Albert Gil, FE.; Aleixos Borrás, MN. (2022). Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study. Lecture Notes in Mechanical Engineering (Online). 294-301. https://doi.org/10.1007/978-3-030-92426-3_3429430

Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

[EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS).Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221113Martelli, G. P., Boscia, D., Porcelli, F., & Saponari, M. (2015). The olive quick decline syndrome in south-east Italy: a threatening phytosanitary emergency. European Journal of Plant Pathology, 144(2), 235-243. doi:10.1007/s10658-015-0784-7Olmo, D., Nieto, A., Adrover, F., Urbano, A., Beidas, O., Juan, A., … Landa, B. B. (2017). First Detection of Xylella fastidiosa Infecting Cherry (Prunus avium) and Polygala myrtifolia Plants, in Mallorca Island, Spain. Plant Disease, 101(10), 1820-1820. doi:10.1094/pdis-04-17-0590-pdnSaponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D., & Saldarelli, P. (2019). Xylella fastidiosa in Olive in Apulia: Where We Stand. Phytopathology®, 109(2), 175-186. doi:10.1094/phyto-08-18-0319-fiVergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., … Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00666Thenkabail, P. S., & Lyon, J. G. (Eds.). (2016). Hyperspectral Remote Sensing of Vegetation. doi:10.1201/b11222Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231-245. doi:10.1016/j.rse.2013.07.031Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sensing, 7(10), 13586-13605. doi:10.3390/rs71013586Hernández-Clemente, R., Navarro-Cerrillo, R., Ramírez, F., Hornero, A., & Zarco-Tejada, P. (2014). A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing, 6(11), 11627-11648. doi:10.3390/rs61111627Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1). doi:10.1038/s41438-018-0043-0Ma, Q., Su, Y., Luo, L., Li, L., Kelly, M., & Guo, Q. (2018). Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310. doi:10.1016/j.ecolind.2018.07.050Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi:10.1109/jstars.2017.2711482Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., … Dandekar, A. M. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1), 1-25. doi:10.1007/s13593-014-0246-1Calderón, R., Navas-Cortés, J., & Zarco-Tejada, P. (2015). Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sensing, 7(5), 5584-5610. doi:10.3390/rs70505584Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., … Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 432-439. doi:10.1038/s41477-018-0189-7Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing, 10(7), 1091. doi:10.3390/rs10071091Vicent, A., & Blasco, J. (2017). When prevention fails. Towards more efficient strategies for plant disease eradication. New Phytologist, 214(3), 905-908. doi:10.1111/nph.14555Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1). doi:10.1186/s13007-018-0324-5Bourgeon, M. A., Gée, C., Debuisson, S., Villette, S., Jones, G., & Paoli, J. N. (2016). « On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices. Precision Agriculture, 18(3), 293-308. doi:10.1007/s11119-016-9489-yUnderwood, J. P., Hung, C., Whelan, B., & Sukkarieh, S. (2016). Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture, 130, 83-96. doi:10.1016/j.compag.2016.09.014Zampetti, E., Papa, P., Di Flaviano, F., Paciucci, L., Petracchini, F., Pirrone, N., … Macagnano, A. (2017). Remotely Controlled Terrestrial Vehicle Integrated Sensory System for Environmental Monitoring. Sensors, 338-343. doi:10.1007/978-3-319-55077-0_43Hiremath, S. A., van der Heijden, G. W. A. M., van Evert, F. K., Stein, A., & ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50. doi:10.1016/j.compag.2013.10.005Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., … Agüera, J. (2015). Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture, 110, 150-161. doi:10.1016/j.compag.2014.11.010Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., & Coppin, P. (2004). Review of methods for in situ leaf area index (LAI) determination. Agricultural and Forest Meteorology, 121(1-2), 37-53. doi:10.1016/j.agrformet.2003.08.001Hosoi, F., & Omasa, K. (2006). Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3610-3618. doi:10.1109/tgrs.2006.881743Stein, M., Bargoti, S., & Underwood, J. (2016). Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry. Sensors, 16(11), 1915. doi:10.3390/s16111915Saponari, M., Boscia, D., Altamura, G., Loconsole, G., Zicca, S., D’Attoma, G., … Martelli, G. P. (2017). Isolation and pathogenicity of Xylella fastidiosa associated to the olive quick decline syndrome in southern Italy. Scientific Reports, 7(1). doi:10.1038/s41598-017-17957-

Product data quality and collaborative engineering

[EN] We survey the impact of product data quality within an extended enterprise framework and present a linguistic model, which focuses on three levels: morphological, syntactic, and semantic.The Spanish Government national R&D Feder program partially sponsored this work as project number 1FD97 0784 “Implementing Design and Manufacturing Advanced Technologies in a Concurrent Engineering Environment. Application to an Automotive Components Manufacturing Company.” We also thank Radiadores Ordoñez, who helped us check the effectiveness of our approachContero, M.; Company Calleja, P.; Vila, C.; Aleixos Borrás, MN. (2002). Product data quality and collaborative engineering. IEEE Computer Graphics and Applications. 22(3):32-42. doi:10.1109/MCG.2002.999786S324222

Development of an Application for the Automatic Evaluation of the Quality of 3D CAD Models

[EN] In the 3D modeling of products, the use of an adequate methodology that ensures the capture of the design intention is very important. The sequence of operations is key, like for instance, the sketches have to be completely restricted and the references of the modelling functions have to be correctly chosen without generating unwanted dependency relationships, among others. In the best of cases, the team leader dictates best practice manuals and then supervises the design work, ensuring that quality, which will facilitate future modifications or new designs based on existing models. However, this is not an established process, causing multiple failures in cascade when modifying or reusing the models is approached. This work has consisted of the development of an application that allows the automation of the quality analysis process in the models and has been developed for the Autodesk Inventor application using its iLogic tool. This work is the result of a Master¿s Thesis, where for the evaluation of the developed application, the examination models of the students of the subject of Graphic Engineering of the 4th year of the Degree in Engineering in Industrial Technologies of the Universitat Politècnica de València have been used.Pou Schmidt, I.; Rodriguez Ortega, A.; Albert Gil, FE.; Aleixos Borrás, MN. (2022). Development of an Application for the Automatic Evaluation of the Quality of 3D CAD Models. Lecture Notes in Mechanical Engineering (Online). 337-344. https://doi.org/10.1007/978-3-030-92426-3_3933734

Estudio de la evolución de la calidad de granada Mollar de Elche durante su maduración usando sistemas de visión artificial

En el presente trabajo se ha estudiado la capacidad de la visión artificial para monitorear la evolución de diferentes propiedades fisicoquímicas de la granada ‘Mollar de Elche’ durante su madurez. Para ello se han obtenido imágenes hiperespectrales y RGB de 30 frutas intactas y sus arilos durante siete recolecciones consecutivas cada 2 semanas (210 frutos en total). En cada fruta se han medido las propiedades de peso, diámetro, sólidos solubles totales (SST), acidez, la actividad antioxidante y el contenido en fenoles totales. Posteriormente, la información espectral (450-1050 nm) y de color (L*, a* y b*) obtenida de las imágenes de las frutas intactas y arilos se ha correlacionado con las propiedades fisicoquímicas mediante el método multivariante de mínimos cuadrados parciales. En el caso de fruta intacta, ambos métodos de visión obtuvieron resultados de predicción similares en todos los parámetros excepto en la actividad antioxidante, donde la imagen hiperespectral fue más precisa. En general, los parámetros predichos por estas técnicas que mayor precisión obtuvieron (R2 = > 0,75; RPD = > 2) fueron el índice de madurez y BrimA, los parámetros de color de los arilos L* y a*, la actividad antioxidante y los fenoles totales. Sin embargo, en el caso de los arilos, la imagen hiperespectral predijo la mayoría de parámetros de manera más precisa (R2 = > 0,75; RPD = > 2) que la imagen RGB, la cual no obtuvo valores de  R2 = > 0,75 y RPD = > 2 en ningún parámetro. Estos resultados indican el gran potencial de la visión artificial, especialmente la imagen hiperespectral, para evaluar las propiedades de calidad de granadas intactas y de los arilos, ofreciendo la posibilidad de determinar el procesamiento al que irán destinadas estas frutas de manera no destructiva y rápida

In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties

[EN] One of the most studied techniques for the non-destructive determination of the internal quality of fruits has been visible and nearinfrared (VIS-NIR) reflectance spectroscopy. This work evaluates a new non-destructive in-line VIS-NIR spectroscopy prototype for in-line identification of five apple varieties, with the advantage that it allows the spectra to be captured with the probe at the same distance from all the fruits regardless of their size. The prototype was tested using varieties with a similar appearance by acquiring the diffuse reflectance spectrum of the fruits travelling on the conveyor belt at a speed of 0.81 m/s which is nearly 1 fruit/s. Principal component analysis (PCA) was used to determine the variables that explain the most variance in the spectra. Seven principal components were then used to perform linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA was found to be the best in-line classification method, achieving 98% and 85% success rates for red and yellow apple varieties, respectively. The results indicated that the in-line application of VIS-NIR spectroscopy that was developed is potentially feasible for the detection of apple varieties with an accuracy that is similar to or better than a laboratory system.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by INIA and FEDER funds through project RTA2015-00078-00-00. Victoria Cortes Lopez thanks the Spanish Ministry of Education, Culture and Sports for FPU grant (FPU13/04202).Cortes-Lopez, V.; Cubero-García, S.; Blasco Ivars, J.; Aleixos Borrás, MN.; Talens Oliag, P. (2019). In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties. Food and Bioprocess Technology. 12(6):1021-1030. https://doi.org/10.1007/s11947-019-02268-0S10211030126Aleixandre-Tudo, J. 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E., Wubbels, A., van der Sluis, C., & van der Meer, J. M. (2006). Understanding factors affecting near infrared analysis of potato constituents. Journal of Near Infrared Spectroscopy, 14(1), 27–35.He, Y., Li, X., & Shao, Y. (2007). Fast discrimination of apple varieties using Vis/NIR spectroscopy. International Journal of Food Properties, 10(1), 9–18.Hernández, A., He, Y., & García, A. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87(3), 303–313.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: with applications in R. New York: springer.Jie, D., Xie, L., Rao, X., & Ying, Y. (2014). Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biology and Technology, 90, 1–6.Kader, A. A., Kasmire, R. F., Mitchell, F. G., Reid, M. S., Sommer, N. F., & Thompson, J. F. (1985). Postharvest technology of horticultural crops (Special publication, mum. 3311, p. 192). Davis: Cooperative Extension, University of California.Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88(7), 1115–1127.Lammertyn, J., De Baerdemaeker, J., & Nicolaï, B. (2000). Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology, 18(2), 121–132.Liu, F., Jiang, Y., & He, Y. (2009). 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H., Crassweller, R. E., & LeBlanc, E. (2005). Electronic nose chemical sensor feasibility study for the differentiation of apple cultivars. American Society of Agricultural Engineers, 48(5), 1995–2002.Martens, H., Nielsen, J. P., & Engelsen, S. B. (2003). Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry, 75(3), 394–404.Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2002). A user-friendly guide to multivariate calibration and classification. Chichester: NIR Publications.Rodríguez-Campos, J., Escalona-Buendía, H. B., Orozco-Avila, I., Lugo-Cervantes, E., & Jaramillo-Flores, M. E. (2011). Dynamics of volatile and non-volatile compounds in cocoa (Theobroma cacao L.) during fermentation and drying processes using principal components analysis. Food Research International, 44(1), 250–258.Ronald, M., & Evans, M. (2016). Classification of selected apple fruit varieties using Naive Bayes. Indian Journal of Computer Science and Engineering, 7(1), 13–19.Sabanci, K., & Ünlersen, M. F. (2016). Different apple varieties classification using kNN and MLP algorithms. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 166–169.Sádecká, J., Jakubíková, M., Májek, P., & Kleinová, A. (2016). Classification of plum spirit drinks by synchronous fluorescence spectroscopy. Food Chemistry, 196, 783–790.Salguero-Chaparro, L., Baeten, V., Abbas, O., & Peña-Rodríguez, F. (2012). On-line analysis of intact olive fruits by vis-NIR spectroscopy: optimisation of the acquisition parameters. Journal of Food Engineering, 112(3), 152–157.Santos, P., Santos, F., Santos, J., & Bezerra, H. (2013). Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. Journal of Data Analysis and Information Processing, 1(03), 30–34.Shang, L., Guo, W., & Nelson, S. O. (2015). Apple variety identification based on dielectric spectra and chemometric methods. Food Anal. Methods, 8(4), 1042–1052.Shao, Y., He, Y., Gómez, A. H., Pereir, A. G., Qiu, Z., & Zhang, Y. (2007). Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicumesculentum) quality characteristics. Journal of Food Engineering, 81(4), 672–678.Shenderey, C., Shmulevich, I., Alchanatis, V., Egozi, H., Hoffman, A., Ostrovsky, V., Lurie, S., Arie, R. B., & Schmilovitch, Z. (2010). NIRS detection of moldy core in apples. Food Bioprocess Technology, 3(1), 79–86.Soares, S. F. C., Gomes, A. A., Galvão Filho, A. R., Araújo, M. C. U., & Galvão, R. K. H. (2013). The successive projections algorithm. Trends in Analytical Chemistry, 42, 84–98.Song, W., Wang, H., Maguire, P., & Nibouche, O. (2017). Differentiation of organic and non-organic apples using near infrared reflectance apectroscopy – a pattern recognition approach. In Unknown host publication (pp. 1–3). https://doi.org/10.1109/ICSENS.2016.7808530 .Sun, X., Liu, Y., Li, Y., Wu, M., & Zhu, D. (2016). Simultaneous measurements of Brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy. Postharvest Biology and Technology, 116, 80–87.Wojdyło, A., Oszmiański, J., & Laskowski, P. (2008). Polyphenolic compounds and antioxidant activity of new and old apple varieties. Journal of Agricultural and Food Chemistry, 56(15), 6520–6530.Wu, X., Wu, B., Sun, J., Li, M., & Du, H. (2016). Discrimination of apples using near infrared spectroscopy and sorting discriminant analysis. International Journal of Food Properties, 19(5), 1016–1028.Wu, X., Wu, B., Sun, J., & Yang, N. (2017). Classification of Apple varieties using near infrared reflectance spectroscopy and fuzzy discriminant C-Means clustering model. Journal of Food Process Engineering, 40, 1–7