Novel modified distribution functions of fiber length in fiber reinforced thermoplastics
Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first tim...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Dayong [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: No title available - an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:182 ; year:2019 ; day:29 ; month:09 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.compscitech.2019.107749 |
|---|
| Katalog-ID: |
ELV047796693 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV047796693 | ||
| 003 | DE-627 | ||
| 005 | 20230626020453.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.compscitech.2019.107749 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000738.pica |
| 035 | |a (DE-627)ELV047796693 | ||
| 035 | |a (ELSEVIER)S0266-3538(19)30622-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Huang, Dayong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| 264 | 1 | |c 2019transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. | ||
| 520 | |a Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. | ||
| 650 | 7 | |a Fiber length factor |2 Elsevier | |
| 650 | 7 | |a Fiber length distribution |2 Elsevier | |
| 650 | 7 | |a Weibull distribution |2 Elsevier | |
| 650 | 7 | |a Shape and scale parameters |2 Elsevier | |
| 700 | 1 | |a Zhao, Xianqiong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |t No title available |d an international journal |g Amsterdam [u.a.] |w (DE-627)ELV013958402 |7 nnns |
| 773 | 1 | 8 | |g volume:182 |g year:2019 |g day:29 |g month:09 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.compscitech.2019.107749 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a GBV_ILN_40 | ||
| 951 | |a AR | ||
| 952 | |d 182 |j 2019 |b 29 |c 0929 |h 0 | ||
| author_variant |
d h dh |
|---|---|
| matchkey_str |
huangdayongzhaoxianqiong:2019----:oemdfedsrbtofntosfielntifbre |
| hierarchy_sort_str |
2019transfer abstract |
| publishDate |
2019 |
| allfields |
10.1016/j.compscitech.2019.107749 doi GBV00000000000738.pica (DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 DE-627 ger DE-627 rakwb eng Huang, Dayong verfasserin aut Novel modified distribution functions of fiber length in fiber reinforced thermoplastics 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier Zhao, Xianqiong oth Enthalten in Elsevier No title available an international journal Amsterdam [u.a.] (DE-627)ELV013958402 nnns volume:182 year:2019 day:29 month:09 pages:0 https://doi.org/10.1016/j.compscitech.2019.107749 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 AR 182 2019 29 0929 0 |
| spelling |
10.1016/j.compscitech.2019.107749 doi GBV00000000000738.pica (DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 DE-627 ger DE-627 rakwb eng Huang, Dayong verfasserin aut Novel modified distribution functions of fiber length in fiber reinforced thermoplastics 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier Zhao, Xianqiong oth Enthalten in Elsevier No title available an international journal Amsterdam [u.a.] (DE-627)ELV013958402 nnns volume:182 year:2019 day:29 month:09 pages:0 https://doi.org/10.1016/j.compscitech.2019.107749 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 AR 182 2019 29 0929 0 |
| allfields_unstemmed |
10.1016/j.compscitech.2019.107749 doi GBV00000000000738.pica (DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 DE-627 ger DE-627 rakwb eng Huang, Dayong verfasserin aut Novel modified distribution functions of fiber length in fiber reinforced thermoplastics 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier Zhao, Xianqiong oth Enthalten in Elsevier No title available an international journal Amsterdam [u.a.] (DE-627)ELV013958402 nnns volume:182 year:2019 day:29 month:09 pages:0 https://doi.org/10.1016/j.compscitech.2019.107749 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 AR 182 2019 29 0929 0 |
| allfieldsGer |
10.1016/j.compscitech.2019.107749 doi GBV00000000000738.pica (DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 DE-627 ger DE-627 rakwb eng Huang, Dayong verfasserin aut Novel modified distribution functions of fiber length in fiber reinforced thermoplastics 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier Zhao, Xianqiong oth Enthalten in Elsevier No title available an international journal Amsterdam [u.a.] (DE-627)ELV013958402 nnns volume:182 year:2019 day:29 month:09 pages:0 https://doi.org/10.1016/j.compscitech.2019.107749 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 AR 182 2019 29 0929 0 |
| allfieldsSound |
10.1016/j.compscitech.2019.107749 doi GBV00000000000738.pica (DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 DE-627 ger DE-627 rakwb eng Huang, Dayong verfasserin aut Novel modified distribution functions of fiber length in fiber reinforced thermoplastics 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier Zhao, Xianqiong oth Enthalten in Elsevier No title available an international journal Amsterdam [u.a.] (DE-627)ELV013958402 nnns volume:182 year:2019 day:29 month:09 pages:0 https://doi.org/10.1016/j.compscitech.2019.107749 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 AR 182 2019 29 0929 0 |
| language |
English |
| source |
Enthalten in No title available Amsterdam [u.a.] volume:182 year:2019 day:29 month:09 pages:0 |
| sourceStr |
Enthalten in No title available Amsterdam [u.a.] volume:182 year:2019 day:29 month:09 pages:0 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Fiber length factor Fiber length distribution Weibull distribution Shape and scale parameters |
| isfreeaccess_bool |
false |
| container_title |
No title available |
| authorswithroles_txt_mv |
Huang, Dayong @@aut@@ Zhao, Xianqiong @@oth@@ |
| publishDateDaySort_date |
2019-01-29T00:00:00Z |
| hierarchy_top_id |
ELV013958402 |
| id |
ELV047796693 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV047796693</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020453.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compscitech.2019.107749</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000738.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047796693</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0266-3538(19)30622-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Dayong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Novel modified distribution functions of fiber length in fiber reinforced thermoplastics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fiber length factor</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fiber length distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weibull distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Shape and scale parameters</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Xianqiong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="t">No title available</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013958402</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:182</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:29</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.compscitech.2019.107749</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">182</subfield><subfield code="j">2019</subfield><subfield code="b">29</subfield><subfield code="c">0929</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Huang, Dayong |
| spellingShingle |
Huang, Dayong Elsevier Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| authorStr |
Huang, Dayong |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV013958402 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Novel modified distribution functions of fiber length in fiber reinforced thermoplastics Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters Elsevier |
| topic |
Elsevier Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters |
| topic_unstemmed |
Elsevier Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters |
| topic_browse |
Elsevier Fiber length factor Elsevier Fiber length distribution Elsevier Weibull distribution Elsevier Shape and scale parameters |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
x z xz |
| hierarchy_parent_title |
No title available |
| hierarchy_parent_id |
ELV013958402 |
| hierarchy_top_title |
No title available |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV013958402 |
| title |
Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| ctrlnum |
(DE-627)ELV047796693 (ELSEVIER)S0266-3538(19)30622-0 |
| title_full |
Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| author_sort |
Huang, Dayong |
| journal |
No title available |
| journalStr |
No title available |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Huang, Dayong |
| container_volume |
182 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Huang, Dayong |
| doi_str_mv |
10.1016/j.compscitech.2019.107749 |
| title_sort |
novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| title_auth |
Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| abstract |
Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. |
| abstractGer |
Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. |
| abstract_unstemmed |
Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 |
| title_short |
Novel modified distribution functions of fiber length in fiber reinforced thermoplastics |
| url |
https://doi.org/10.1016/j.compscitech.2019.107749 |
| remote_bool |
true |
| author2 |
Zhao, Xianqiong |
| author2Str |
Zhao, Xianqiong |
| ppnlink |
ELV013958402 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.compscitech.2019.107749 |
| up_date |
2024-07-06T17:08:15.799Z |
| _version_ |
1803850293759180800 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV047796693</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020453.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compscitech.2019.107749</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000738.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047796693</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0266-3538(19)30622-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Dayong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Novel modified distribution functions of fiber length in fiber reinforced thermoplastics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Amongst the present prediction models of mechanical properties, the Weibull distribution has been introduced to describe the fiber length distribution. However, the Weibull distribution cannot capture the long tail decay, leading to underestimation of the fiber length factor (χ 2). For the first time, two novel modified distribution functions are proposed based on Erlang-2 distribution and Weibull distribution, respectively. The modified Erlang distribution is derived from introducing a shape parameter to capture the sharp peak better, whereas the modified Weibull distribution is developed by introducing a polynomial function instead of the exponential function to reduce the decay rate for the long fibers. The modified distribution functions are investigated by experimental data reported in literatures and the experimental data of GFRPA66 we obtained. The relationship between the shape parameters (α and β) and k can be described by a linear growth function when α >β, and the slope of α~k is larger than that of β~k. The scale parameters (λ, λ α and λ β ) can be considered as the linear growth function of l n. The relationship between the improvement in fiber length factor (Δ) and the improvement in number-average fiber length (δ) can be described by a fitting power function with an offset. In comparison to the Weibull distribution, the modified distribution functions improve more than 4% in χ 2. The modified Weibull distribution tends to capture the actual fiber length distribution for LFT, whereas the modified Erlang distribution captures that for SFT.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fiber length factor</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fiber length distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weibull distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Shape and scale parameters</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Xianqiong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="t">No title available</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013958402</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:182</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:29</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.compscitech.2019.107749</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">182</subfield><subfield code="j">2019</subfield><subfield code="b">29</subfield><subfield code="c">0929</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.400405 |