Internal bond （IB） strength is one of the most important me- chanical properties that indicate particleboard quality. The aim of this study was to find a simple regression model that considers the most important par...Internal bond （IB） strength is one of the most important me- chanical properties that indicate particleboard quality. The aim of this study was to find a simple regression model that considers the most important parameters that can influence on IB strength. In this study, IB strength was predicted by three kinds of equations （linear, quadratic, and exponential） that were based on the percentage of adhesive （8%, 9.5%, and 11%）, particle size （＋5, -5 ＋8, -8 12, and -12 mesh）, and density （0.65, 0.7, and 0.75 g/cm3）. Our analysis of the results （using SHAZAM 9 software） showed that the exponential function best fitted the experi- mental data and predicted the IB strength with 18~,/0 error. In order de- crease the error percentage, the Buckingham Pi theorem was used to build regression models for predicting IB strength based on particle size,展开更多
Based on Buckingham＇s π-Theorem, dimensional analysis has achieved considerable success over the past near-century. Model testing has long been a powerful tool in both scientific studies and engineering applications...Based on Buckingham＇s π-Theorem, dimensional analysis has achieved considerable success over the past near-century. Model testing has long been a powerful tool in both scientific studies and engineering applications. However, the prototype objects are becoming more and more complicated nowadays, and many of the prototype systems can contain several sub-systems. The conventional theories on model-prototype similarity and dimensional analysis have only limited application since the π-Theorem itself does not distinguish between the original system and subsystems. This is particularly true in the field of structural dynamics, where the structure is often modeled as a multi-degree-of-freedom system. In this paper, we attempt to show that, if a system can be decoupled into several nontrivial subsystems, then, in each subsystem, the number of π-terms will be reduced and therefore simplify the model testing. On the other hand, if a system cannot be decoupled into subsystems, then using model testing with reduced π-term analysis, both experimentally and theoretically, may introduce severe errors.展开更多
文摘Internal bond （IB） strength is one of the most important me- chanical properties that indicate particleboard quality. The aim of this study was to find a simple regression model that considers the most important parameters that can influence on IB strength. In this study, IB strength was predicted by three kinds of equations （linear, quadratic, and exponential） that were based on the percentage of adhesive （8%, 9.5%, and 11%）, particle size （＋5, -5 ＋8, -8 12, and -12 mesh）, and density （0.65, 0.7, and 0.75 g/cm3）. Our analysis of the results （using SHAZAM 9 software） showed that the exponential function best fitted the experi- mental data and predicted the IB strength with 18~,/0 error. In order de- crease the error percentage, the Buckingham Pi theorem was used to build regression models for predicting IB strength based on particle size,
文摘Based on Buckingham＇s π-Theorem, dimensional analysis has achieved considerable success over the past near-century. Model testing has long been a powerful tool in both scientific studies and engineering applications. However, the prototype objects are becoming more and more complicated nowadays, and many of the prototype systems can contain several sub-systems. The conventional theories on model-prototype similarity and dimensional analysis have only limited application since the π-Theorem itself does not distinguish between the original system and subsystems. This is particularly true in the field of structural dynamics, where the structure is often modeled as a multi-degree-of-freedom system. In this paper, we attempt to show that, if a system can be decoupled into several nontrivial subsystems, then, in each subsystem, the number of π-terms will be reduced and therefore simplify the model testing. On the other hand, if a system cannot be decoupled into subsystems, then using model testing with reduced π-term analysis, both experimentally and theoretically, may introduce severe errors.
