Wood Material Science and Engineering最新文献

{"title":"Predicting the mechanical properties of timber from existing structures by the longitudinal vibration method, visual grading and definition of the nominal cross-section","authors":"Carlos Osuna-Sequera, Francisco Arriaga, Daniel F. Llana, Guillermo Íñiguez-González","doi":"10.1080/17480272.2023.2243673","DOIUrl":"https://doi.org/10.1080/17480272.2023.2243673","url":null,"abstract":"ABSTRACTThe modulus of elasticity (MOE), bending strength (MOR) and density of 45 150 × 200 × 5400 mm3 Salzmann pine timber pieces from an existing eighteenth century structure were obtained by testing and also estimated by non-destructive testing. Density was estimated by means of the drilling technique and the MOE and MOR by recording the longitudinal natural frequency of vibration. Four methods for defining the dimensions of the nominal cross-section were applied to establish which was most adequate to address the high variability in the cross-sections along the length. MOE and MOR linear regression results showed r2 = 59–68% using dynamic MOE, relative edge knot diameter and slope of grain as independent variables. The nominal cross-section defined as the mean cross-section area along the length proved to be the most effective for estimating mechanical properties, followed by the nominal cross-section defined as the average measurements of the piece taken at the middle part or central third along its length. The longitudinal vibration method enables the acquisition of superior predictive models compared to time-of-flight measurement-based methods.KEYWORDS: Bending strengthin-situ assessmentmodulus of elasticitydrillingresonancetimber AcknowledgmentsMinisterio de Economía y Competitividad [Spanish Ministry of Economy and Competitiveness]. Plan Nacional I+D 2013–2016. Proj.: BIA 2014-55089-P. We would like to thank Mr. Antonio Arce from Intrama S.A. for the free supply of timber. Partial financial support (OTT D200, English revision) by Universidad Politécnica de Madrid, Spain. The technical support offered by Ramón García Lombardero for measurements was greatly appreciated.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":368077,"journal":{"name":"Wood Material Science and Engineering","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136214758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonic pulse velocity for mechanical properties determination of wood","authors":"Soheil Palizi, Vahab Toufigh, Moein Ramezanpour Kami","doi":"10.1080/17480272.2023.2208556","DOIUrl":"https://doi.org/10.1080/17480272.2023.2208556","url":null,"abstract":"ABSTRACTWood is a widely used material in various industries, and its mechanical properties are crucial to determine the final product's performance. Modulus of elasticity (MOE) is one of the most important mechanical properties of wood, which measures its stiffness and ability to resist deformation under applied loads. The objective of this research was to create robust models for predicting the moduli of elasticity (MOE) of wood species in three principal directions under varying moisture levels, using the ultrasonic pulse velocity (UPV) technique. The study employed three modeling techniques, namely multivariable linear regression (MLR), artificial neural network (ANN), and support vector regression (SVR). The research involved developing 72 models using different input variables and methods, and then training and testing them with experimental data. Our findings revealed that the SVR model utilizing the radial basis kernel function (RBF) and Levenberg-Marquardt backpropagation ANN exhibited the lowest mean squared error (MSE) and the highest correlation coefficient, with R2 values of 0.979 and 0.989, respectively. These results suggest that the developed models and the UPV technique could significantly enhance the precision of MOE predictions for wood species, presenting a valuable tool for industries that utilize wood in their products.KEYWORDS: Ultrasonic pulse velocitymodulus of elasticitywoodmachine learning Data availabilitySome or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Near infrared spectrometry based on partial least squares.2 Levenberg–Marquardt back propagation algorithm was chosen as the training algorithm.3 The gradient descent with a momentum back propagation algorithm.4 ANN model was trained by resilient backpropagation.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.","PeriodicalId":368077,"journal":{"name":"Wood Material Science and Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135477893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical, optical and barrier properties of PLA-layered silicate nanocomposites coated with organic plasma polymer thin films","authors":"S. Ligot, S. Benali, R. Ramy-Ratiarison, Iulian Marius Murariu, R. Snyders, P. Dubois","doi":"10.24218/msear.2015.04","DOIUrl":"https://doi.org/10.24218/msear.2015.04","url":null,"abstract":"In the frame of the efforts that are nowadays provided to develop new environmentally-friendly products as biosourced alternative to petrochemical polymers, we investigated in this work new PLA-based materials for packaging applications obtained by combining bulk and surface modifications of PLA substrates. Four PLA-based nanocomposites were prepared by adding organo-modified layered silicates (either Cloisite® 30B or Cloisite® 20A) and a nucleating agent, i.e. N,N’-ethylenebisstearamide (EBS). The combination of EBS with CL30B led to very good nanofiller dispersion into PLA-clay nanocomposite while allowing, unlike neat PLA, to preserve the structural and thermal properties of PLA under plasma treatment. Based on this study, PLA/CL30B/EBS nanocomposites have been selected to investigate their surface modification that consisted in depositing on this substrate an organic barrier coating, i.e. an ethyl lactate plasma polymer film (ELPPF) synthesized by plasma polymerization of ethyl lactate. The PLA/CL30B/EBSELPPF system allowed increasing the tensile modulus from 3800 MPa (uncoated film) to 5200 MPa at high power plasma while preserving the ultimate mechanical properties. In addition, optical properties study showed that PLA/CL30B/EBS-ELPPF is also the best UV-B protective material while keeping a good transparency. Finally, the oxygen transmission rate was reduced by 53% with respect to neat PLA. All properties of final material are discussed as a function of the characteristics of PLA-clay nanocomposite substrate, of the ethyl lactate plasma polymer film (ELPPF) and of the substrate/plasma film interface.","PeriodicalId":368077,"journal":{"name":"Wood Material Science and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126657261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}