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Model analysis of worm gear pair system using finite element analysis

Barshikar Raghavendra Rajendra, Baviskar Prasad R., Patil Milind M., Dube Anil S., Dhore Vishal J.

International Journal of Applied Mechanics and Engineering

2024

Vol. 29, no. 2

1--15

Spur gear, helical gear, worm gear, and bevel gear are all important components in industrial applications such as vehicles, pushes, conveyors, elevators, bowl mill, rolling mills, ribbon blender, machine tools, aeroplanes, and windmills. When various types of defects, such as wear, tooth breakage, corrosion, and scratches on bearings, appear in gearboxes, normal machine function may be abruptly terminated. As a result, output and dependability suffer. As a result, several quality tracking and evaluation approaches have been adopted by companies. Finite element analysis (FEA) is one of the approaches. This research paper presents the FEA of a ribbon blender worm gear pair by using Ansys 18.0 to identify the weak gear of the worm gear pair, natural frequency, and deformation. Proe-5 utilized for creation of three-dimensional geometry of threaded worm and toothed worm wheels, as well as other related elements such as shafts and bearings. Steel is used for the worm, shaft, and bearing, whereas bronze is used for the worm wheel. Ansys 18.0 is implemented to carry out worm gear pair model analysis. The results demonstrate that the worm wheel had the most deformation when compared to the worm, and that the natural frequency is greater than the operational frequency of the worm gear pair. The findings of the research study, worm wheel deteriorate early than worm, model analysis plays a significant role in vibration monitoring of worm gear pair, and this work is valuable for further fault analysis of ribbon blender worm gearbox utilising vibration response.

Defect categorization of ribbon blender worm gearbox worm wheel and bearing based on artificial neural network

Barshikar Raghavendra Rajendra, Ghongade Harshvardhan P., Bhadre Anjali A., Pawar Harjitkumar U., Rane Harshal S.

Eksploatacja i Niezawodność

2024

Vol. 26, no. 2

art. no. 185371

There is a demand for worm gearboxes in diversified industrial fields that include machinery such as escalators, ribbon blenders, pulverisers, bowl mills, etc. because of their peculiar characteristics like torque and quick retardation. The most commonly occurring defects in a worm gear box are scratches that develop in the worm gear and in bearings. Early defect categorization is required to prevent a sudden breakdown that would decrease production. The defect is depicted in different cases, which include defects in the gear tooth and the outer and inner races of the bearing. In another case, the defect is considered in the gear tooth as well as the bearing. The severity is designated using the ANN. The experiments were performed under these conditions with a good worm gearbox to capture vibration response signatures. Using these values as an input to the ANN, the model is trained. Experimental results show that vibration amplitude increases with fault progression in the worm gearbox, and the trained ANN model effectively categorizes worm gearbox faults with an accuracy of 97.12%.