Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
In this study,to efficiently remove Pb(Ⅱ) from aqueous environments,a novel L-serine-modified polyethylene/polypropylene nonwoven fabric sorbent(NWF-serine)was fabricated through the radiation grafting of glycidyl me...In this study,to efficiently remove Pb(Ⅱ) from aqueous environments,a novel L-serine-modified polyethylene/polypropylene nonwoven fabric sorbent(NWF-serine)was fabricated through the radiation grafting of glycidyl methacrylate and subsequent L-serine modification.The effect of the absorbed dose was investigated in the range of 5–50 kGy.NWF-serine was characterized by Fourier transform infrared spectroscopy,thermogravimetric analysis,and scanning electron microscopy.Batch adsorption tests were conducted to investigate the influences of pH,adsorption time,temperature,initial concentration,and sorbent dosage on the Pb(Ⅱ) adsorption performance of NWF-serine.The results indicated that Pb(Ⅱ) adsorption onto NWF-serine was an endothermic process,following the pseudo-second-order kinetic model and Langmuir isotherm model.The saturated adsorption capacity was 198.1 mg/g.NWF-serine exhibited Pb(Ⅱ) removal rates of 99.8% for aqueous solutions with initial concentrations of 100 mg/L and 82.1% for landfill leachate containing competitive metal ions such as Cd,Cu,Ni,Mn,and Zn.Furthermore,NWF-serine maintained 86% of its Pb(Ⅱ) uptake after five use cycles.The coordination of the carboxyl and amino groups with Pb(Ⅱ) was confirmed using X-ray photoelectron spectroscopy and extended X-ray absorption fine structure analysis.展开更多
Nonwoven fabrics have become indispensable materials across various industries due to their versatility,durability,and cost-effectiveness.These fabrics are manufactured by bonding or interlocking fibers using mechanic...Nonwoven fabrics have become indispensable materials across various industries due to their versatility,durability,and cost-effectiveness.These fabrics are manufactured by bonding or interlocking fibers using mechanical,thermal,or chemical processes,instead of weaving or knitting.The nonwoven fabric market has witnessed significant growth in recent years,driven by technological advancements,increasing demand from various enduse industries,and shifting consumer preferences towards sustainable and eco-friendly materials.This article delves into the innovations and demand surge propelling the growth of the nonwoven fabric market.展开更多
Phase change materials have a key role for wearable thermal management,but suffer from poor water vapor permeability,low enthalpy value and weak shape stability caused by liquid phase leakage and intrinsic rigidity of...Phase change materials have a key role for wearable thermal management,but suffer from poor water vapor permeability,low enthalpy value and weak shape stability caused by liquid phase leakage and intrinsic rigidity of solid–liquid phase change materials.Herein,we report for the first time a versatile strategy for designed assembly of high-enthalpy flexible phase change nonwovens(GB-PCN)by wet-spinning hybrid grapheneboron nitride(GB)fiber and subsequent impregnating paraffins(e.g.,eicosane,octadecane).As a result,our GB-PCN exhibited an unprecedented enthalpy value of 206.0 J g^(−1),excellent thermal reliability and anti-leakage capacity,superb thermal cycling ability of 97.6%after 1000 cycles,and ultrahigh water vapor permeability(close to the cotton),outperforming the reported PCM films and fibers to date.Notably,the wearable thermal management systems based on GB-PCN for both clothing and face mask were demonstrated,which can maintain the human body at a comfortable temperature range for a significantly long time.Therefore,our results demonstrate huge potential of GB-PCN for human-wearable passive thermal management in real scenarios.展开更多
The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated ...The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated to establish a good mechanism for product performance regulation. In this study, we first used Wilcoxon test and Pearson correlation analysis to investigate the effect of intercalation rate on structural variables and product performance. Then, regression models were constructed to predict the values of each structural variable under different combinations of process parameters. Finally, we constructed a multi-objective constrained optimization problem based on the stepwise regression model and the product variable conditions. The problem was solved using the NSGA-II algorithm. The optimal was achieved when the acceptance distance was 2.892 cm and the hot air speed was 2000 r/min.展开更多
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金supported by the National Natural Science Foundation of China(Nos.11605275 and 11675247)。
文摘In this study,to efficiently remove Pb(Ⅱ) from aqueous environments,a novel L-serine-modified polyethylene/polypropylene nonwoven fabric sorbent(NWF-serine)was fabricated through the radiation grafting of glycidyl methacrylate and subsequent L-serine modification.The effect of the absorbed dose was investigated in the range of 5–50 kGy.NWF-serine was characterized by Fourier transform infrared spectroscopy,thermogravimetric analysis,and scanning electron microscopy.Batch adsorption tests were conducted to investigate the influences of pH,adsorption time,temperature,initial concentration,and sorbent dosage on the Pb(Ⅱ) adsorption performance of NWF-serine.The results indicated that Pb(Ⅱ) adsorption onto NWF-serine was an endothermic process,following the pseudo-second-order kinetic model and Langmuir isotherm model.The saturated adsorption capacity was 198.1 mg/g.NWF-serine exhibited Pb(Ⅱ) removal rates of 99.8% for aqueous solutions with initial concentrations of 100 mg/L and 82.1% for landfill leachate containing competitive metal ions such as Cd,Cu,Ni,Mn,and Zn.Furthermore,NWF-serine maintained 86% of its Pb(Ⅱ) uptake after five use cycles.The coordination of the carboxyl and amino groups with Pb(Ⅱ) was confirmed using X-ray photoelectron spectroscopy and extended X-ray absorption fine structure analysis.
文摘Nonwoven fabrics have become indispensable materials across various industries due to their versatility,durability,and cost-effectiveness.These fabrics are manufactured by bonding or interlocking fibers using mechanical,thermal,or chemical processes,instead of weaving or knitting.The nonwoven fabric market has witnessed significant growth in recent years,driven by technological advancements,increasing demand from various enduse industries,and shifting consumer preferences towards sustainable and eco-friendly materials.This article delves into the innovations and demand surge propelling the growth of the nonwoven fabric market.
基金supported by the National Natural Science Foundation of China(Nos.21903082,22003065,22125903,51872283,22075279,21805273,22273100)Dalian Innovation Support Plan for High Level Talents(2019RT09)+3 种基金Dalian National Laboratory For Clean Energy(DNL),CAS,DNL Cooperation Fund,CAS(DNL201912,DNL201915,DNL202016,DNL202019)DICP(DICP I2020032,DICP I202036,I202218)The Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2021002,YLU-DNL 2021007,YLU-DNL 2021009)Q.Shi would like to thank Dalian Outstanding Young Scientific Talent Program(Grant 2019RJ10).
文摘Phase change materials have a key role for wearable thermal management,but suffer from poor water vapor permeability,low enthalpy value and weak shape stability caused by liquid phase leakage and intrinsic rigidity of solid–liquid phase change materials.Herein,we report for the first time a versatile strategy for designed assembly of high-enthalpy flexible phase change nonwovens(GB-PCN)by wet-spinning hybrid grapheneboron nitride(GB)fiber and subsequent impregnating paraffins(e.g.,eicosane,octadecane).As a result,our GB-PCN exhibited an unprecedented enthalpy value of 206.0 J g^(−1),excellent thermal reliability and anti-leakage capacity,superb thermal cycling ability of 97.6%after 1000 cycles,and ultrahigh water vapor permeability(close to the cotton),outperforming the reported PCM films and fibers to date.Notably,the wearable thermal management systems based on GB-PCN for both clothing and face mask were demonstrated,which can maintain the human body at a comfortable temperature range for a significantly long time.Therefore,our results demonstrate huge potential of GB-PCN for human-wearable passive thermal management in real scenarios.
文摘The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated to establish a good mechanism for product performance regulation. In this study, we first used Wilcoxon test and Pearson correlation analysis to investigate the effect of intercalation rate on structural variables and product performance. Then, regression models were constructed to predict the values of each structural variable under different combinations of process parameters. Finally, we constructed a multi-objective constrained optimization problem based on the stepwise regression model and the product variable conditions. The problem was solved using the NSGA-II algorithm. The optimal was achieved when the acceptance distance was 2.892 cm and the hot air speed was 2000 r/min.
