Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation Algorithm. (2022)
- Record Type:
- Journal Article
- Title:
- Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation Algorithm. (2022)
- Main Title:
- Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation Algorithm
- Authors:
- Jena, Sourav
Jeet, Siddharth
Bagal, Dilip Kumar
Baliarsingh, Asini Kumar
Nayak, Dillip Ranjan
Barua, Abhishek - Abstract:
- Abstract: Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating conditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the control unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta -heuristic algorithms i.e. Material Generation Algorithm and Sunflower Optimization Algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both Material Generation Algorithm and Sunflower Optimization Algorithm resulted to be precise in providing better output as compared to Taguchi method for maximizing the output power and efficiency of theAbstract: Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating conditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the control unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta -heuristic algorithms i.e. Material Generation Algorithm and Sunflower Optimization Algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both Material Generation Algorithm and Sunflower Optimization Algorithm resulted to be precise in providing better output as compared to Taguchi method for maximizing the output power and efficiency of the industrial reducer gearbox. … (more)
- Is Part Of:
- Materials today. Volume 50:Part 5(2022)
- Journal:
- Materials today
- Issue:
- Volume 50:Part 5(2022)
- Issue Display:
- Volume 50, Issue 5, Part 5 (2022)
- Year:
- 2022
- Volume:
- 50
- Issue:
- 5
- Part:
- 5
- Issue Sort Value:
- 2022-0050-0005-0005
- Page Start:
- 1113
- Page End:
- 1122
- Publication Date:
- 2022
- Subjects:
- Mechanical Reducer -- Taguchi Method -- Material Generation Algorithm -- Sunflower Optimization Algorithm
Materials science -- Congresses -- Periodicals
620.1 - Journal URLs:
- http://www.sciencedirect.com/science/journal/22147853 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.matpr.2021.08.005 ↗
- Languages:
- English
- ISSNs:
- 2214-7853
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20876.xml