A study of application of Six Sigma in improving process optimization for e-bike pedal mandrels

Considered as one of the key parts in the industry, the pedal mandrels on electric bikes will affect the comfort to cyclists. This study, based on the DMAIC model in Six Sigma skill and with the robust design of the Taguchi method, investigated empirically the cutting process of pedal mandrels on electric bikes. A Back-Propagation Neural Network was employed in combination with Taguchi method. Finally, the genetic algorithm was employed to determine the optimized parameters for quality, whereby to arrive at the optimal process parameters. The optimized processing parameters were put to tolerance analysis to establish the reasonable tolerances for the processing parameters for fitting parts. Workpiece assembly was made more easily by optimizing the process; as a result, the quality of pedal mandrel was improved. Taguchi method and Back-Propagation Neural Network coupled with genetic algorithm were used to determine the optimal combinations of parameters; and the findings showed that process capability index by the latter was 1.41, which outperformed Taguchi method with 1.34. Reasonable tolerance method was further used, where regressive analysis was employed to reveal the radii of the tool tip, cutting depths and feed depths. When the parameters were set within the upper and lower limits of the ruler, any process executed within such limits results in optimal process value.