Study of Carbon Black Types in SBR Rubber: Mechanical and Vibration Damping Properties

Performance of Full-Component Coal Gasification Fine Slag: High-Value Utilization as Reinforcing Material in Styrene-Butadiene Rubber (ESBR) for Replacing Carbon Black

Ultrafine, highly active coal gasification slag (HCGS) was produced via a sustainable, green dry-ball-milling method. Coal gasification fine slag (CGS), a potential environmental pollutant, was used as a new source of rubber filler without pre-treatment, enabling waste utilisation. HCGS was added to styrene-butadiene rubber (ESBR) composites, and the effects of HCGS and the filler content on the mechanical and thermal stabilities of SBR were evaluated. The procedure conforms to important green metrics, requiring no solvent or additional reagent, or solvent-assistance for product collection. HCGS reduced the scorch time (t10) and curing time (t90) of the filled ESBR composites relative to those of pure SBR and improved the mechanical parameters. The tensile strength at 50 phr reached 10.91 MPa, and the tear strength at 90 phr reached 64.92 kN/m, corresponding to 9.4- and 3.92-fold increases relative to that of SBR filled with HCGS, respectively. HCGS exerted a reinforcing effect on ESBR, comparable to that of commercial carbon black (CB) N330. HCGS improves the binding between rubber molecules and filler particles and captures the rubber chain, thereby limiting its movement. HCGS is potentially applicable as a CB substitute in the rubber industry, with environmental and economic benefits in the disposal of CGS.

Calcium-Lignosulfonate-Filled Rubber Compounds Based on NBR with Enhanced Physical-Mechanical Characteristics

Calcium lignosulfonate in the amount 30 phr was incorporated into rubber compounds based on pure NBR and an NBR carbon black batch, in which the content of carbon black was 25 phr. Glycerine, as a cheap and environmentally friendly plasticizer, was applied into both types of rubber formulations in a concentration scale ranging from 5 to 20 phr. For the cross-linking of rubber compounds, a sulfur-based curing system was used. The work was aimed at the investigation of glycerine content on the curing process and rheological properties of rubber compounds, cross-link density, morphology and physical-mechanical properties of vulcanizates. The results show that glycerine influences the shapes of curing isotherms and results in a significant decrease between the maximum and minimum torque. This points to the strong plasticizing effect of glycerine on rubber compounds, which was also confirmed from rheological measurements. The application of glycerine resulted in better homogeneity of the rubber compounds and in the better dispersion and distribution of lignosulfonate within the rubber matrix, which was subsequently reflected in the significant improvement of tensile characteristics of vulcanizates. A higher cross-link density as well as better physical-mechanical properties were exhibited by the vulcanizates based on the carbon black batch due to the presence of a reinforcing filler.

Vibration and Trajectory Tracking Control of Engineering Mechanical Arm Based on Neural Network

We offer a neural network-based control method to control the vibration of the engineering mechanical arm and the trajectory in order to solve the problem of large errors in tracking the path when the engineering mechanical arm is unstable and under the influence of the outside world. A mechanical arm network is used to perform tasks related to learning the unknown dynamic properties of a engineering mechanical arms keyboard without the need for prior learning. Given the dynamic equations of the engineering mechanical arm, the dynamic properties of the mechanical arm were studied using a positive feedback network. The adaptive neural network management system was developed, and the stability and integrity of the closed-loop system were proved by Lyapunov’s function. Engineering mechanical arm motion trajectory control errors were modeled and validated in the Matlab/Simulink environment. The simulation results show that the management of the adaptive neural network is able to better control the desired path of the engineering mechanical arm in the presence of external interference, and the fluctuation range of input torque is small. The PID control has a large error in the expected trajectory tracking of the engineering mechanical arm, the fluctuation range of the input torque is as high as 20, and the jitter phenomenon is more serious. The use of detailed comparisons and adaptive neural network monitoring can perform well in manipulating the trajectory of the engineering mechanical arm. The engineering mechanical arm uses an adaptive neural network control method, in which the control precision of engineering mechanical arm motion trajectory can be improved and the out-of-control phenomenon of mechanical arm motion can be reduced.