As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan ba...As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.展开更多
in order to increase its hardness and gravity as well as dimension stability, the technology of hotcompressing on P8ulownla wood was studied. The main factors of affecting the spring back of the compressedPaulownis sa...in order to increase its hardness and gravity as well as dimension stability, the technology of hotcompressing on P8ulownla wood was studied. The main factors of affecting the spring back of the compressedPaulownis samples were discussed. It was discovered that every factor in the experiment had obvious effects onwood hardness and dimension stability of compressed wood. When the MC (Moisture Content) of experimentalspecimens was 13.89%, it was useful to spray water on the surface of samples before hot pressing. The best reSult was the recovery of compression set could decrease from 90.69O/O of untreated wood to 45.51 % of soakingspecimens into PF (Phenol Formaldehyde) water solution. The hot pressing time was 8 min at 190℃.展开更多
基金supported by China Postdoctoral Science Foundation(2019M651240)National Natural Science Foundation of China(31670559).
文摘As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.
文摘in order to increase its hardness and gravity as well as dimension stability, the technology of hotcompressing on P8ulownla wood was studied. The main factors of affecting the spring back of the compressedPaulownis samples were discussed. It was discovered that every factor in the experiment had obvious effects onwood hardness and dimension stability of compressed wood. When the MC (Moisture Content) of experimentalspecimens was 13.89%, it was useful to spray water on the surface of samples before hot pressing. The best reSult was the recovery of compression set could decrease from 90.69O/O of untreated wood to 45.51 % of soakingspecimens into PF (Phenol Formaldehyde) water solution. The hot pressing time was 8 min at 190℃.
