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 method to assess the response efficiency of the Jidoka-DTs system is investigated. The main contributions of this paper are as follows:. • For the proposed method to assess the response efficiency of the Jidoka-DTs system, the application process is investigated. • In future work, an Jidoka-DTs management and control system that integrates modeling, interaction, response efficiency feedback, and optimal control is urgently needed to face more complex and diverse HRC scenarios. • Improve machine learning algorithms by hybridizing XGboost with one of the LSTM algorithms. • Build an Jidoka-DTs simulator self-training recruitment system to tackle massive and imbalanced data that compared with VMD-LSTM [ 18 ] and appears superior in some zone of implementation. • Improve work [ 25 ] sustainability where weakness zones reduction by >23.21 % with an increasing system performance >70.98 %. 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 / c m 3 . It should also have the right rupture modulus ( RM = 83 : 134 kgf / c m 2 ), elasticity ( 3.3 × 10 4 : 4.5 × 10 4 kgf / c m 2 ) , and compressive strength ( 6.7 : 6.83 MPa ) . The OEE is increased to 89.13 %, when the quality increased up to 99.98 and performance to 89.15 %.