Light conveyor belts. Determination of the electrostatic field generated by a running light conveyor belt

2013 ◽  
Keyword(s):  
Electrostatic Field ◽  
Conveyor Belt ◽  
Conveyor Belts

FATIGUE STRENGTH IMPROVEMENT OF STEEL ROLLERS IN CONVEYOR BELTS WHEN USING SHOT PEENING

Tribologia ◽  
2019 ◽  
Vol 284 (2) ◽  
pp. 143-149
Author(s):  
Grzegorz Zając ◽  
Jerzy Michnej
Keyword(s):  
Fatigue Strength ◽  
Shot Peening ◽  
Conveyor Belt ◽  
Precise Determination ◽  
Roller Surface ◽  
Compressive Stresses ◽  
Fatigue Strength Improvement ◽  
Conveyor Belts ◽  
Strength Improvement

Numerous factors influence the tribological wear of steel rollers used in conveyor belts. The difficulty in precise determination of these factors results from, among others, a complex and not completely explored wear mechanism. Corrosive and fatigue damages of conveyor belt rollers start mainly on the surface. The present article describes studies on the influence of processing technology of the roller surface by means of the shot peening method in the context of fatigue strength improvement. Compressive stresses formed due to shot peening reach the maximum value just below the roller surface. The values of compressive stresses reach about half of the proof stress of the steel from which they are made. In results of the carried out experimental model tests, it was found that it is possible to significantly improve the properties of the top surface layer of the roller surface, and especially to increase fatigue strength, fatigue corrosion resistance, resistance to cracking caused by stress corrosion, and the like.

The Calculation of Force Effects of a Conveyor Belt of the Pipe Conveyor to Forming Rollers by FEM

2015 ◽  
Vol 806 ◽  
pp. 181-186
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Peter Michalik ◽  
Nikoleta Husáková ◽  
Ivica Ristović ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Methods ◽  
Conveyor Belt ◽  
Calculation Model ◽  
Present Information ◽  
Conveyor Belts ◽  
Original Calculation

The paper interprets the calculation of the size of force effects created by the reason of the effect of balled conveyor belt of pipe conveyor to the guide rollers located in the ring idler stations. Knowing them is essential for the research of dynamic resistances by pipe conveyors operations. Examined force effects were determined by the help of finite element methods. The calculation was performed by the program Abaqus. Presented calculation is realized by original calculation model. The results of the calculation present information about action of the effect force of the balled conveyor belt to the guide rolls in the ring idler station. Results became a basis for determination of postulates for identification of dynamic resistances. Their knowledge is the key element for conveyor belts lifetime increase.

Mine conveyor belt fire classification

2021 ◽  
pp. 073490412110563
Author(s):  
Manuel J Barros-Daza ◽  
Kray D Luxbacher ◽  
Brian Y Lattimer ◽  
Jonathan L Hodges
Keyword(s):  
Input Parameter ◽  
Conveyor Belt ◽  
Classification Model ◽  
Design Parameters ◽  
Air Velocity ◽  
Conveyor Belts ◽  
Artificial Neural ◽  
Mine Roof ◽  
Test Scenarios

This article presents a conveyor belt fire classification model that allows for the determination of the most effective firefighting strategy. In addition, the effect of belt design parameters on the fire classification was determined. A methodology that involves the use of numerical simulations and artificial neural networks was implemented. An approach previously proposed for modeling fires over conveyor belts was used. With the objective of obtaining some required modeling input parameter and verifying the capacity of this approach to get realistic results, computational fluid dynamics model calibration and validation were carried out using experimental test results available in the literature. Results indicated that scenarios with belt positions closer to the mine roof and greater tunnel heights require a higher longitudinal air velocity to be attacked directly. Furthermore, the belt fire classification model provided by the artificial neural network had an accuracy around 95% when test scenarios were classified.

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