Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable
The time period that a motorcyclist is exposed to in the event of an accident is very short, during which he is exposed to severe forces in the head area, which may lead to death. There is no escape from securing the head with a helmet that can withstand these shocks and is impenetrable. High-qualit...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025001926 |
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| author | Ahmed M. Abed Ahmed Fathy Radwa A. El Behairy Tamer S Gaafar |
| author_facet | Ahmed M. Abed Ahmed Fathy Radwa A. El Behairy Tamer S Gaafar |
| author_sort | Ahmed M. Abed |
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| description | The time period that a motorcyclist is exposed to in the event of an accident is very short, during which he is exposed to severe forces in the head area, which may lead to death. There is no escape from securing the head with a helmet that can withstand these shocks and is impenetrable. High-quality helmets rely on feeding their plastic-alumina dough with rice straw (RS) and called (RSA) that meets the ISO 8611 standard. The suggested RS weights in the dough are 3.6, 8.9, and 11.25 wt.% with sizes 2.1, 3.45, and 8.7 × 10(-2) cm, and dryness levels are among 85–91 %. The dough has been tested via a Digital Twin (DT) simulator that relies on human dexterity in mapping the helmet surface as a finite element (FEM) that is called Jidoka-DT. The mixture machine is connected to many sensors that track the values of significant parameters, such as temperature, moisture, viscosity, Reynolds number, crack resistance, and compressibility, that affect helmet manufacturing via injecting RSA composition towards mould. The FEM classified via XGboost algorithm to divide the helmet surface according to the severity of ground shock and analyses the forces via Long Short-Term Memory (LSTM) that are hybridised to meet the benefits of both. LSTM is used to determine the helmet weakness zones (WZ) and XGboost classifies product parts according to physical and mechanical property. The standard dough should have a density of between 1.16 and 1.4 g/cm3. It should also have the right rupture modulus (RM=83:134kgf/cm2), elasticity (3.3×104:4.5×104kgf/cm2), and compressive strength (6.7:6.83MPa). The OEE is increased to 89.13 %, when the quality increased up to 99.98 and performance to 89.15 %. |
| format | Article |
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| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-4703fe9784964935a7692da0baa9844a2025-01-27T04:22:10ZengElsevierResults in Engineering2590-12302025-03-0125104104Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrableAhmed M. Abed0Ahmed Fathy1Radwa A. El Behairy2Tamer S Gaafar3Department of Industrial Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al Kharj, 16273, Saudi Arabia; Industrial Engineering Department, Zagazig University, Zagazig P.O. Box 44519, Egypt; Corresponding author.Electrical Engineering Department, Faculty of Engineering, Jouf University, Saudi Arabia; Electrical engineer department, Faculty of Engineering, Zagazig University, P.O. Box 44519, EgyptSoil and Water Department, faculty of agriculture, Tanta University, Tanta, 31527, EgyptComputers and Systems Department, Zagazig University, Zagazig P.O. Box 44519, EgyptThe time period that a motorcyclist is exposed to in the event of an accident is very short, during which he is exposed to severe forces in the head area, which may lead to death. There is no escape from securing the head with a helmet that can withstand these shocks and is impenetrable. High-quality helmets rely on feeding their plastic-alumina dough with rice straw (RS) and called (RSA) that meets the ISO 8611 standard. The suggested RS weights in the dough are 3.6, 8.9, and 11.25 wt.% with sizes 2.1, 3.45, and 8.7 × 10(-2) cm, and dryness levels are among 85–91 %. The dough has been tested via a Digital Twin (DT) simulator that relies on human dexterity in mapping the helmet surface as a finite element (FEM) that is called Jidoka-DT. The mixture machine is connected to many sensors that track the values of significant parameters, such as temperature, moisture, viscosity, Reynolds number, crack resistance, and compressibility, that affect helmet manufacturing via injecting RSA composition towards mould. The FEM classified via XGboost algorithm to divide the helmet surface according to the severity of ground shock and analyses the forces via Long Short-Term Memory (LSTM) that are hybridised to meet the benefits of both. LSTM is used to determine the helmet weakness zones (WZ) and XGboost classifies product parts according to physical and mechanical property. The standard dough should have a density of between 1.16 and 1.4 g/cm3. It should also have the right rupture modulus (RM=83:134kgf/cm2), elasticity (3.3×104:4.5×104kgf/cm2), and compressive strength (6.7:6.83MPa). The OEE is increased to 89.13 %, when the quality increased up to 99.98 and performance to 89.15 %.http://www.sciencedirect.com/science/article/pii/S2590123025001926RSA-PlasticIndustrial helmetLSTMXGboostJidokafinite element |
| spellingShingle | Ahmed M. Abed Ahmed Fathy Radwa A. El Behairy Tamer S Gaafar Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable Results in Engineering RSA-Plastic Industrial helmet LSTM XGboost Jidoka finite element |
| title | Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable |
| title_full | Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable |
| title_fullStr | Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable |
| title_full_unstemmed | Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable |
| title_short | Jidoka-DT simulator programmed by hybridize XGboost-LSTM to evaluate helmets quality produced by Rice-Straw-alumina plastic dough to resist shocks and impenetrable |
| title_sort | jidoka dt simulator programmed by hybridize xgboost lstm to evaluate helmets quality produced by rice straw alumina plastic dough to resist shocks and impenetrable |
| topic | RSA-Plastic Industrial helmet LSTM XGboost Jidoka finite element |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025001926 |
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