Indian Journal of Science and Technology
Year: 2018, Volume: 11, Issue: 29, Pages: 1-7
Jorge Duarte Forero1*, Guillermo E. Valencia1 and Luis G. Obregon2
1 Department of Mechanical Engineering, KAI Research Group, Universidad del Atlantico, Barranquilla, Colombia; [email protected], [email protected]
2 Department of Chemical Engineering, Sustainable Chemical and Biochemical Processes Research Group, Universidad del Atlantico, Barranquilla, Colombia; [email protected]
*Author for correspondence
Jorge Duarte Forero,
Department of Mechanical Engineering, KAI Research Group, Universidad del Atlantico, Barranquilla, Colombia; [email protected]
Background/Objectives: The implementation of Design of Experiments as a tool for the study of process variables related to the operation of some types of industrial machinery, such as Continuous Tracked Vehicles, as a base for optimization processes that allow establishing an operational point with the most efficient use of these vehicles. Methods: Using a factorial design, a series of experiments were realized, with some process variables as design factors, each one arranged with two design levels. From these, statistical methods were used to validate the normal distribution of the results and at the same time, to establish mathematical expressions that correlate said parameters. Findings: The results allowed the determination of the factors that can be neglected in optimization processes, due to their low influence of the operation characteristics; besides, it was demonstrated that certain parameters, as the terrain, have little or no influence on the vehicle performance, which serves as a valid criterion to evaluate future investments inload transport systems. Application: To develop a tool for the prediction of the operation characteristics of Continuous Tracked Vehicles which will be used for the estimation of potential savings generated by more efficient use of these machines?
Keywords: Continuous Tracked Vehicles, Design of Experiments, Operation Characteristics, Performance, Statistical Model
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