{'fraction_non_alphanumeric': 0.018867924528301886, 'fraction_numerical': 0.004851752021563342, 'mean_word_length': 5.105263157894737, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimization of Operations in Epicyclic Gear Train Modelled Through Computer Aided Design

A rotating power transmission unit, namely gears experience contact stresses during meshing, all of which can be visualised in the teeth, which is in contact with another gear enabling power transmission. This article presents an optimisation approach in which the developed stresses between contacts can be minimised by selecting an epicyclic gear train having a specific set of parameters, which will be discussed in the article. To conduct this experiment with a variation of different control factors, Taguchi’s Design of Experiment (DoE) methodology was considered, because it minimises the number of test runs to find an optimal level of solution to suffice the goal of optimisation as compared to the factorial design methodology. In addition, the analysis of variance (ANOVA) methodology has been employed to study the significance of each input factor on the responses selected for carrying out the experiment. Computer Aided Design (CAD) software packages, namely, Solidworks 2013, for creation of the 3D assembly design, and ANSYSV13, for the analysis of stresses, were used. The parameters for noise, input factors and responses are so chosen, that it is feasible to modify and control the data in the environment of the software packages, as per requirement. To perform the DoE and analysis of data, the Minitab 16 software package was used. The goal of this experiment was to find an optimised set of various input factors for which the stresses would be the minimum, and hence tooth failure chances could be minimised. This combination of DoE with CAD is an approach which will not only reduce the time and effort to conduct the experiment in real life, but will also reduce and eliminate the various costs associated with conducting the experiment.